IEEE Projects 2013-2014 - DataMining - Project Titles and Abstracts
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Elysium PRO Titles with Abstracts 2018-19
In this paper we propose the application of a novel associative classifier, the Heaviside's Classifier, for
the early detection of Age-Related Macular Degeneration un retinal fundus images. Retinal fundus
images are, first, processed by a simple method based on the Homomorphic filtering and some basic
mathematical morphology operations; in the second phase we extract relevant features of the images
using the Zernike moments, we also apply a feature selection method to select the best features from
the original features set. The dataset created from the images with the best features are used to train
and test a new classification model whose learning and classification phases are based on the
Heaviside's Function. Experimental results show that our method is capable to achieve an accuracy
value about the 94.12% with a dataset created from images belonging to famous image repositories.
EPRO
BIOM - 001
Detection of Age-Related Macular Degeneration in Fundus Images by an Associative
Classifier.
Lung cancer is one of the leading causes of death worldwide. Several computer-aided diagnosis
systems have been developed to help reduce lung cancer mortality rates. This paper presents a novel
structural co-occurrence matrix (SCM)-based approach to classify nodules into malignant or benign
nodules and also into their malignancy levels. The SCM technique was applied to extract features from
images of nodules and classifying them into malignant or benign nodules and also into their malignancy
levels. The computed tomography exams from the lung image database consortium and image database
resource initiative datasets provide information concerning nodule positions and their malignancy
levels. The SCM was applied on both grayscale and Hounsfield unit images with four filters, to wit,
mean, Laplace, Gaussian, and Sobel filters creating eight different configurations. The classification
stage used three well-known classifiers: multilayer perceptron, support vector machine, and k-nearest
neighbors algorithm and applied them to two tasks: (i) to classify the nodule images into malignant or
benign nodules and (ii) to classify the lung nodules into malignancy levels (1 to 5). The results of this
approach were compared to four other feature extraction methods: gray-level co-occurrence matrix,
local binary patterns, central moments, and statistical moments.
EPRO
BIOM - 002
Health of Things Algorithms for Malignancy Level Classification of Lung Nodules.
Elysium PRO Titles with Abstracts 2018-19
Dempster-Shafer evidence theory (DS theory) is widely used in brain magnetic resonance imaging
(MRI) segmentation, due to its efficient combination of the evidence from different sources. In this
paper, an improved MRI segmentation method, which is based on fuzzy c-means (FCM) and DS
theory, is proposed. Firstly, the average fusion method is used to reduce the uncertainty and the conflict
information in the pictures. Then, the neighborhood information and the different influences of spatial
location of neighborhood pixels are taken into consideration to handle the spatial information. Finally,
the segmentation and the sensor data fusion are achieved by using the DS theory. The simulated images
and the MRI images illustrate that our proposed method is more effective in image segmentation.
EPRO
BIOM - 003
Improved evidential fuzzy c-means method.
Retinal microaneurysms (MAs) are the earliest clinical sign of diabetic retinopathy disease. Detection
of MAs is crucial for the early diagnosis of diabetic retinopathy and prevention of blindness. In this
paper, a novel and reliable method for automatic detection of MAs in retinal images is proposed. In the
first stage of the proposed method, several preliminary microaneurysm candidates are extracted using
a gradient weighting technique and an iterative thresholding approach. In the next stage, in addition to
intensity and shape descriptors, a new set of features based on local convergence index filters is
extracted for each candidate. Finally, the collective set of features is fed to a hybrid sampling/boosting
classifier to discriminate the MAs from non-MAs candidates. The method is evaluated on images with
different resolutions and modalities (color and scanning laser ophthalmoscope) using six publicly
available data sets including the retinopathy online challenges (ROC) data set. The proposed method
achieves an average sensitivity score of 0.471 on the ROC data set outperforming state-of-the-art
approaches in an extensive comparison. The experimental results on the other five data sets
demonstrate the effectiveness and robustness of the proposed MAs detection method regardless of
different image resolutions and modalities.
EPRO
BIOM - 004
Retinal Micro aneurysms Detection Using Local Convergence Index Features.
Elysium PRO Titles with Abstracts 2018-19
Image segmentation is critical and challenging in computer vision and medical image analysis. Despite
decades of research, existing segmentation algorithms are still subject to typical segmentation
problems, such as over-segmentation, under-segmentation, and non-closed and spurious edges. In this
paper, taking the carpal bones from hand X-ray images as the foreground regions, we propose a
segmentation approach to integrate segmentations from region-based and boundary-based methods to
tackle these typical segmentation problems. First, adaptive local thresholding and adaptive Canny edge
detection are explored to extract foreground regions and the edge map. Second, the integration of the
edge map and foreground regions by XORing is proposed, to tackle the over-segmentation by adding
a background boundary from the edge map near the carpal bone boundary so as to break the connection
between the foreground and the over-segmented background, to handle under-segmentation by adding
a foreground boundary from the edge map near the carpal bone boundary so as to enclose the missing
foreground due to under-segmentation, and to complement non-closed edge and spurious edge from
the edge map through the carpal bone regions from the local adaptive thresholding. Optionally, marker-
controlled watershed segmentation or an active contourbased method is employed to refine the
integrated segmentation.
EPRO
BIOM - 005
Delineation of Carpal Bones from Hand X-Ray Images through Prior Model, and
Integration of Region-Based and Boundary-Based Segmentations.
Single-image super-resolution (SR) reconstruction via sparse representation has recently attracted
broad interest. It is known that a low-resolution (LR) image is susceptible to noise or blur due to the
degradation of the observed image, which would lead to a poor SR performance. In this paper, we
propose a novel robust edge-preserving smoothing SR (REPS-SR) method in the framework of sparse
representation. An EPS regularization term is designed based on gradient-domain-guided filtering to
preserve image edges and reduce noise in the reconstructed image. Furthermore, a smoothing-aware
factor adaptively determined by the estimation of the noise level of LR images without manual
interference is presented to obtain an optimal balance between the data fidelity term and the proposed
EPS regularization term. An iterative shrinkage algorithm is used to obtain the SR image results for
LR images. The proposed adaptive smoothing-aware scheme makes our method robust to different
levels of noise. Experimental results indicate that the proposed method can preserve image edges and
reduce noise and outperforms the current state-of-the-art methods for noisy images.
EPRO
BIOM - 006
Robust Single-Image Super-Resolution Based on Adaptive Edge-Preserving
Smoothing Regularization.
Elysium PRO Titles with Abstracts 2018-19
To achieve the better segmentation performance, we propose a unified algorithm for automatic glioma
segmentation. In this paper, we first use spatial fuzzy c-mean clustering to estimate region-of-interest
in multimodal MRI images, and then extract some seed points from there for region growing based on
a new notion “affinity”. In the end, we design a two-step strategy to refine the glioma border with
region merging and improved distance regularization level set method. In BRATS 2015 database, we
evaluate the accuracy and robustness of our method with performance scores, including dice, positive
predictive value (PPV), and sensitivity metrics, as well as Hausdorff and Euclidean distance (HD&ED).
The high metric values (dice = 0.86, PPV = 0.90, and sensitivity = 0.84) and small distance errors (HD
= 14.39 mm and ED = 3.31 mm) indicate a remarkable accuracy. Also, we observe the ranking is No.1
in terms of dice and PPV, comparing with the state-of-the-art methods. In addition, the robustness is
also at a high-level due to the refinement structure. And Spearman's rank coefficient test verities a
significant correlation between the high-grade gliomas and low-grade gliomas. Overall, the proposed
method is effective in segmenting gliomas in multimodal images or flair images, and has the potential
in routine examinations of gliomas in daily clinical practice.
EPRO
BIOM - 007
Glioma Segmentation with a Unified Algorithm in Multimodal MRI Images.
This paper develops a new dimensionality reduction method, named biomimetic uncorrelated locality
discriminant projection (BULDP), for face recognition. It is based on unsupervised discriminant
projection and two human bionic characteristics: principle of homology continuity and principle of
heterogeneous similarity. With these two human bionic characteristics, we propose a novel adjacency
coefficient representation, which does not only capture the category information between different
samples, but also reflects the continuity between similar samples and the similarity between different
samples. By applying this new adjacency coefficient into the unsupervised discriminant projection, it
can be shown that we can transform the original data space into an uncorrelated discriminant subspace.
A detailed solution of the proposed BULDP is given based on singular value decomposition. Moreover,
we also develop a nonlinear version of our BULDP using kernel functions for nonlinear dimensionality
reduction. The performance of the proposed algorithms is evaluated and compared with the state-of-
the-art methods on four public benchmarks for face recognition. Experimental results show that the
proposed BULDP method and its nonlinear version achieve much competitive recognition
performance.
EPRO
BIOM - 008
BULDP: Biomimetic Uncorrelated Locality Discriminant Projection for Feature
Extraction in Face Recognition.
Elysium PRO Titles with Abstracts 2018-19
Material decomposition allows the reconstruction of material-specific images in spectral X-ray
imaging, which requires efficient decomposition models. Due to the presence of nonideal effects in X-
ray imaging systems, it is difficult to explicitly estimate the imaging systems for material
decomposition tasks. As an alternative to previous empirical material decomposition methods, we
investigated material decomposition using ensemble learning methods in this paper. Three ensemble
methods with two decision trees as the base learning algorithms were investigated to perform material
decomposition in both simulation and experiment. The results were quantitatively evaluated for
comparison studies. In general, the results demonstrate that the proposed ensemble learning methods
often outperform their base learning algorithms, and rarely reduce performance. Compared to the
reference methods and its base learning algorithm, the performance of the Boosting method using
REPTree with regularization is improved by over 42% and 13%, respectively, in the noiseless
simulated scenario of the XCAT phantom with cardiac and respiratory motion, and over 36% and 17%,
respectively, in the noisy scenario. Simultaneously, the performance is improved by over 9% and 8%,
respectively, in the original torso phantom scenario, and over 13% and 12%, respectively, in the
denoising scenario. The results indicate that ensemble learning with gradient descent optimization
algorithms is more appropriate for material decomposition tasks.
EPRO
BIOM - 009
Material Decomposition Using Ensemble Learning for Spectral X-ray Imaging.
In positron emission tomography (PET) image reconstruction, the Bayesian framework with various
regularization terms has been implemented to constrain the radio tracer distribution. Varying the
regularizing weight of a maximum a posteriori (MAP) algorithm specifies a lower bound of the tradeoff
between variance and spatial resolution measured from the reconstructed images. The purpose of this
paper is to build a patch-based image enhancement scheme to reduce the size of the unachievable
region below the bound and thus to quantitatively improve the Bayesian PET imaging. We cast the
proposed enhancement as a regression problem which models a highly nonlinear and spatial-varying
mapping between the reconstructed image patches and an enhanced image patch. An artificial neural
network model named multilayer perceptron (MLP) with backpropagation was used to solve this
regression problem through learning from examples. Using the BrainWeb phantoms, we simulated
brain PET data at different count levels of different subjects with and without lesions. The MLP was
trained using the image patches reconstructed with a MAP algorithm of different regularization
parameters for one normal subject at a certain count level. To evaluate the performance of the trained
MLP, reconstructed images from other simulations and two patient brain PET imaging data sets were
processed.
EPRO
BIOM - 010
Artificial Neural Network Enhanced Bayesian PET Image Reconstruction.
Elysium PRO Titles with Abstracts 2018-19
Automated segmentation of fine objects details in a given image is becoming of crucial interest in
different imaging fields. In this paper, we propose a new variational level-set model for both global
and interactive\selective segmentation tasks, which can deal with intensity inhomogeneity and the
presence of noise. The proposed method maintains the same performance on clean and noisy vector-
valued images. The model utilizes a combination of locally computed denoising constrained surface
and a denoising fidelity term to ensure a fine segmentation of local and global features of a given
image. A two-phase level-set formulation has been extended to a multi-phase formulation to
successfully segment medical images of the human brain. Comparative experiments with state-of-the-
art models show the advantages of the proposed method.
EPRO
BIOM - 011
Image Segmentation for Intensity Inhomogeneity in Presence of High Noise.
Optical endomicroscopy (OEM) is an emerging technology platform with preclinical and clinical
imaging applications. Pulmonary OEM via fibre bundles has the potential to provide in vivo, in situ
molecular signatures of disease such as infection and inflammation. However,a enhancing the quality
of data acquired by this technique for better visualization and subsequent analysis remains a
challenging problem. Cross coupling between fiber cores and sparse sampling by imaging fiber bundles
are the main reasons for image degradation, and poor detection performance (i.e., inflammation,
bacteria, etc.). In this paper, we address the problem of deconvolution and restoration of OEM data.
We propose a hierarchical Bayesian model to solve this problem and compare three estimation
algorithms to exploit the resulting joint posterior distribution. The first method is based on Markov
chain Monte Carlo methods, however, it exhibits a relatively long computational time. The second and
third algorithms deal with this issue and are based on a variational Bayes approach and an alternating
direction method of multipliers algorithm, respectively. Results on both synthetic and real datasets
illustrate the effectiveness of the proposed methods for restoration of OEM images.
EPRO
BIOM - 012
Deconvolution and Restoration of Optical Endomicroscopy Images.
Elysium PRO Titles with Abstracts 2018-19
Natural image quality assessment (NIQA) wins increasing attention, while NIQA models are rarely
used in the medical community. A couple of studies employ the NIQA methodologies for medical
image quality assessment (MIQA), but building the benchmark data sets necessitates considerable time
and professional skills. In particular, the characteristics of synthesized distortions are different from
those of clinical distortions, which make the results not so convincing. In clinic, signal-to-noise ratio
(SNR) is widely used, which is defined as the quotient of the mean signal intensity measured in a tissue
region of interest (ROI) and the standard deviation of the signal intensity in an air region outside the
imaged object, and both regions are outlined by specialists. We take advantage of the knowledge that
SNR is routinely used and concern whether SNR measure can perform as a baseline metric for the
development of MIQA algorithms. To address the issue, the inter-observer reliability of SNR measure
is investigated regarding to different tissue ROIs [white matter (WM); cerebral spinal fluid (CSF)] in
magnetic resonance (MR) images. A total of 192 T2, 88 T1, 76 T2 and 55 contrast-enhanced T1 (T1C)
weighted images are analyzed. Statistical analysis indicates that SNR values show consistency between
different observers to the same ROI in each modality (Wilcoxon rank sum test, pw ≥ 0.11; and paired
sample t-test, pp 0.28).
EPRO
BIOM - 013
Can Signal-to-Noise Ratio Perform as a Baseline Indicator for Medical Image
Quality Assessment.
X-ray tensor tomography (XTT) is a novel imaging modality for the three-dimensional reconstruction
of X-ray scattering tensors from dark-field images obtained in a grating interferometry setup. The two-
dimensional dark-field images measured in XTT are degraded by noise effects, such as detector readout
noise and insufficient photon statistics, and consequently, the three-dimensional volumes reconstructed
from this data exhibit noise artifacts. In this paper, we investigate the best way to incorporate a
denoising technique into the XTT reconstruction pipeline, i.e., the popular total variation denoising
technique. We propose two different schemes of including denoising in the reconstruction process, one
using a column block-parallel iterative scheme and one using a whole-system approach. In addition,
we compare the results when using a simple denoising approach applied either before or after
reconstruction. The effectiveness is evaluated qualitatively and quantitatively based on datasets from
an industrial sample and a clinical sample. The results clearly demonstrate the superiority of including
denoising in the reconstruction process, along with slight advantages of the whole-system approach.
EPRO
BIOM - 014
Incorporating a Noise Reduction Technique Into X-Ray Tensor Tomography.
Elysium PRO Titles with Abstracts 2018-19
Cardiac bi-ventricle segmentation can help physicians to obtain clinical indices, such as mass and
volume of left ventricle (LV) and right ventricle (RV). In this paper, we propose a regression
segmentation framework to delineate boundaries of bi-ventricle from cardiac magnetic resonance (MR)
images by building a regression model automatically and accurately. First, we extract DAISY feature
from images. Then, a point based representation method is employed to depict the boundaries. Finally,
we use DAISY as input and boundary points as labels to train the regression model based on deep
belief network. Regression combined deep learning and DAISY feature can capture high level image
information and accurately segment biventricle with fewer assumptions and lower computational cost.
In our experiment, the performance of the proposed framework is compared with manual segmentation
on 145 clinical subjects (2900 images in total), which are collected from three hospitals affiliated with
two health care centers (London Healthcare Center and St. Josephs HealthCare). The results of our
method and manually segmented method are highly consistent. High Pearson's correlation coefficient
between automated boundaries and manual annotation is up to 0.995 (endocardium of LV), 0.997
(epicardium of LV), and 0.985 (RV). Average Dice metric is up to 0.916 (endocardium of LV), 0.941
(epicardium of LV), and 0.844 (RV). Altogether, experimental results are capable of demonstrating the
efficacy of our regression segmentation framework for cardiac MR images.
EPRO
BIOM - 015
Deep Regression Segmentation for Cardiac Bi-Ventricle MR Images.
Automated 3-D breast ultrasound has been proposed as a complementary modality to mammography
for early detection of breast cancers. To facilitate the interpretation of these images, computer aided
detection systems are being developed in which mass segmentation is an essential component for
feature extraction and temporal comparisons. However, automated segmentation of masses is
challenging because of the large variety in shape, size, and texture of these 3-D objects. In this paper,
the authors aim to develop a computerized segmentation system, which uses a seed position as the only
priori of the problem. A two-stage segmentation approach has been proposed incorporating shape
information of training masses. At the first stage, a new adaptive region growing algorithm is used to
give a rough estimation of the mass boundary. The similarity threshold of the proposed algorithm is
determined using a Gaussian mixture model based on the volume and circularity of the training masses.
In the second stage, a novel geometric edge-based deformable model is introduced using the result of
the first stage as the initial contour. In a data set of 50 masses, including 38 malignant and 12 benign
lesions, the proposed segmentation method achieved a mean Dice of 0.74 ± 0.19 which outperformed
the adaptive region growing with a mean Dice of 0.65 ± 0.2 (p-value <; 0.02).
EPRO
BIOM - 016
Mass Segmentation in Automated 3-D Breast Ultrasound Using Adaptive Region
Growing and Supervised Edge-Based Deformable Model.
Elysium PRO Titles with Abstracts 2018-19
The use of appearance and shape priors in image segmentation is known to improve accuracy; however,
existing techniques have several drawbacks. For instance, most active shape and appearance models
require landmark points and assume unimodal shape and appearance distributions, and the level set
representation does not support construction of local priors. In this paper, we present novel appearance
and shape models for image segmentation based on a differentiable implicit parametric shape
representation called a disjunctive normal shape model (DNSM). The DNSM is formed by the
disjunction of polytopes, which themselves are formed by the conjunctions of half-spaces. The
DNSM's parametric nature allows the use of powerful local prior statistics, and its implicit nature
removes the need to use landmarks and easily handles topological changes. In a Bayesian inference
framework, we model arbitrary shape and appearance distributions using nonparametric density
estimations, at any local scale. The proposed local shape prior results in accurate segmentation even
when very few training shapes are available, because the method generates a rich set of shape variations
by locally combining training samples. We demonstrate the performance of the framework by applying
it to both 2-D and 3-D data sets with emphasis on biomedical image segmentation applications.
EPRO
BIOM - 017
Image Segmentation Using Disjunctive Normal Bayesian Shape and Appearance
Models.
Auscultation is one of the most used techniques for detecting cardiovascular diseases, which is one of
the main causes of death in the world. Heart murmurs are the most common abnormal finding when a
patient visits the physician for auscultation. These heart sounds can either be innocent, which are
harmless, or abnormal, which may be a sign of a more serious heart condition. However, the accuracy
rate of primary care physicians and expert cardiologists when auscultating is not good enough to avoid
most of both type-I (healthy patients are sent for echocardiogram) and type-II (pathological patients
are sent home without medication or treatment) errors made. In this paper, the authors present a novel
convolutional neural network based tool for classifying between healthy people and pathological
patients using a neuromorphic auditory sensor for FPGA that is able to decompose the audio into
frequency bands in real time. For this purpose, different networks have been trained with the heart
murmur information contained in heart sound recordings obtained from nine different heart sound
databases sourced from multiple research groups. These samples are segmented and preprocessed using
the neuromorphic auditory sensor to decompose their audio information into frequency bands and, after
that, sonogram images with the same size are generated. These images have been used to train and test
different convolutional neural network architectures. The best results have been obtained with a
modified version of the AlexNet model, achieving 97% accuracy (specificity: 95.12%, sensitivity:
93.20%, and type-II errors.
EPRO
BIOM - 018
Deep Neural Networks for the Recognition and Classification of Heart Murmurs
Using Neuromorphic Auditory Sensors.
Elysium PRO Titles with Abstracts 2018-19
The prominent advantage of meshfree method, is the way to build the representation of computational
domain, based on the nodal points without any explicit meshing connectivity. Therefore, meshfree
method can conveniently process the numerical computation inside interested domains with large
deformation or inhomogeneity. In this paper, we adopt the idea of meshfree representation into cardiac
medical image analysis in order to overcome the difficulties caused by large deformation and
inhomogeneous materials of the heart. In our implementation, as element-free Galerkin method can
efficiently build a meshfree representation using its shape function with moving least square fitting,
we apply this meshfree method to handle large deformation or inhomogeneity for solving cardiac
segmentation and motion tracking problems. We evaluate the performance of meshfree representation
on a synthetic heart data and an in-vivo cardiac MRI image sequence. Results showed that the error of
our framework against the ground truth was 0.1189 ± 0.0672 while the error of the traditional FEM
was 0.1793 ± 0.1166. The proposed framework has minimal consistency constraints, handling large
deformation and material discontinuities are simple and efficient, and it provides a way to avoid the
complicated meshing procedures while preserving the accuracy with a relatively small number of
nodes.
EPRO
BIOM - 019
A Meshfree Representation for Cardiac Medical Image Computing.
In this paper, we aim to produce a realistic 2-D projection of the breast parenchymal distribution from
a 3-D breast magnetic resonance image (MRI). To evaluate the accuracy of our simulation, we compare
our results with the local breast density (i.e., density map) obtained from the complementary full-field
digital mammogram. To achieve this goal, we have developed a fully automatic framework, which
registers MRI volumes to X-ray mammograms using a subject-specific biomechanical model of the
breast. The optimization step modifies the position, orientation, and elastic parameters of the breast
model to perform the alignment between the images. When the model reaches an optimal solution, the
MRI glandular tissue is projected and compared with the one obtained from the corresponding
mammograms. To reduce the loss of information during the ray-casting, we introduce a new approach
that avoids resampling the MRI volume. In the results, we focus our efforts on evaluating the agreement
of the distributions of glandular tissue, the degree of structural similarity, and the correlation between
the real and synthetic density maps. Our approach obtained a high-structural agreement regardless the
glandularity of the breast, whilst the similarity of the glandular tissue distributions and correlation
between both images increase in denser breasts. Furthermore, the synthetic images show continuity
with respect to large structures in the density maps.
EPRO
BIOM - 020
Multimodal Breast Parenchymal Patterns Correlation Using a Patient-Specific
Biomechanical Model.
Elysium PRO Titles with Abstracts 2018-19
The analysis of gait dynamics is helpful for predicting and improving the quality of life, morbidity, and
mortality in neuro-degenerative patients. Feature extraction of physiological time series and
classification between gait patterns of healthy control subjects and patients are usually carried out on
the basis of 1-D signal analysis. The proposed approach presented in this paper departs itself from
conventional methods for gait analysis by transforming time series into images, of which texture
features can be extracted from methods of texture analysis. Here, the fuzzy recurrence plot algorithm
is applied to transform gait time series into texture images, which can be visualized to gain insight into
disease patterns. Several texture features are then extracted from fuzzy recurrence plots using the gray-
level co-occurrence matrix for pattern analysis and machine classification to differentiate healthy
control subjects from patients with Parkinson's disease, Huntington's disease, and amyotrophic lateral
sclerosis. Experimental results using only the right stride-intervals of the four groups show the
effectiveness of the application of the proposed approach.
EPRO
BIOM - 021
Texture Classification and Visualization of Time Series of Gait Dynamics in Patients
with Neuro-Degenerative Diseases.
The analysis of the pure motion of subnuclear structures without influence of the cell nucleus motion
and deformation is essential in live cell imaging. In this paper, we propose a 2-D contour-based image
registration approach for compensation of nucleus motion and deformation in fluorescence microscopy
time-lapse sequences. The proposed approach extends our previous approach, which uses a static
elasticity model to register cell images. Compared with that scheme, the new approach employs a
dynamic elasticity model for the forward simulation of nucleus motion and deformation based on the
motion of its contours. The contour matching process is embedded as a constraint into the system of
equations describing the elastic behavior of the nucleus. This results in better performance in terms of
the registration accuracy. Our approach was successfully applied to real live cell microscopy image
sequences of different types of cells including image data that was specifically designed and acquired
for evaluation of cell image registration methods. An experimental comparison with the existing
contour-based registration methods and an intensity-based registration method has been performed.
We also studied the dependence of the results on the choice of method parameters.
EPRO
BIOM - 022
Non-Rigid Contour-Based Registration of Cell Nuclei in 2-D Live Cell Microscopy
Images Using a Dynamic Elasticity Model.
Elysium PRO Titles with Abstracts 2018-19
Automated optic disk (OD) detection plays an important role in developing a computer aided system
for eye diseases. In this paper, we propose an algorithm for the OD detection based on structured
learning. A classifier model is trained based on structured learning. Then, we use the model to achieve
the edge map of OD. Thresholding is performed on the edge map, thus a binary image of the OD is
obtained. Finally, circle Hough transform is carried out to approximate the boundary of OD by a circle.
The proposed algorithm has been evaluated on three public datasets and obtained promising results.
The results (an area overlap and Dices coefficients of 0.8605 and 0.9181, respectively, an accuracy of
0.9777, and a true positive and false positive fraction of 0.9183 and 0.0102) show that the proposed
method is very competitive with the state-of-the-art methods and is a reliable tool for the segmentation
of OD.
EPRO
BIOM - 023
Optic Disk Detection in Fundus Image Based on Structured Learning.
Objective: Diabetic retinopathy (DR) is characterized by the progressive deterioration of retina with
the appearance of different types of lesions that include micro-aneurysms, hemorrhages, exudates, etc.
Detection of these lesions plays a significant role for early diagnosis of DR. Methods: To this aim, this
paper proposes a novel and automated lesion detection scheme, which consists of the four main steps:
vessel extraction and optic disc removal, preprocessing, candidate lesion detection, and postprocessing.
The optic disc and the blood vessels are suppressed first to facilitate further processing. Curvelet-based
edge enhancement is done to separate out the dark lesions from the poorly illuminated retinal
background, while the contrast between the bright lesions and the background is enhanced through an
optimally designed wideband bandpass filter. The mutual information of the maximum matched filter
response and the maximum Laplacian of Gaussian response are then jointly maximized. Differential
evolution algorithm is used to determine the optimal values for the parameters of the fuzzy functions
that determine the thresholds of segmenting the candidate regions. Morphology-based postprocessing
is finally applied to exclude the falsely detected candidate pixels. Results and Conclusions: Extensive
simulations on different publicly available databases highlight an improved performance over the
existing methods with an average accuracy of 97.71 % and robustness in detecting the various types of
DR lesions irrespective of their intrinsic properties.
EPRO
BIOM - 024
Automatic Detection of Retinal Lesions for Screening of Diabetic Retinopathy.
Elysium PRO Titles with Abstracts 2018-19
Low-dose computed tomography (LDCT) images are often highly degraded by amplified mottle noise
and streak artifacts. Maintaining image quality under low-dose scan protocols is a well-known
challenge. Recently, sparse representation-based techniques have been shown to be efficient in
improving such CT images. In this paper, we propose a 3D feature constrained reconstruction (3D-
FCR) algorithm for LDCT image reconstruction. The feature information used in the 3D-FCR
algorithm relies on a 3D feature dictionary constructed from available high quality standard-dose CT
sample. The CT voxels and the sparse coefficients are sequentially updated using an alternating
minimization scheme. The performance of the 3D-FCR algorithm was assessed through experiments
conducted on phantom simulation data and clinical data. A comparison with previously reported
solutions was also performed. Qualitative and quantitative results show that the proposed method can
lead to a promising improvement of LDCT image quality.
EPRO
BIOM - 025
3D Feature Constrained Reconstruction for Low-Dose CT Imaging.
This study explored the hidden biomedical information from knee MR images for osteoarthritis (OA)
prediction. We have computed the Cartilage Damage Index (CDI) information from 36 informative
locations on tibiofemoral cartilage compartment from 3D MR imaging and used PCA analysis to
process the feature set. Four machine learning methods (artificial neural network (ANN), support
vector machine (SVM), random forest and naïve Bayes) were employed to predict the progression of
OA, which was measured by change of Kellgren and Lawrence (KL) grade, Joint Space Narrowing on
Medial compartment (JSM) grade and Joint Space Narrowing on Lateral compartment (JSL) grade. To
examine the different effect of medial and lateral informative locations, we have divided the 36-
dimensional feature set into 18-dimensional medial feature set and 18-dimensional lateral feature set
and run the experiment on four classifiers separately. Experiment results showed that the medial feature
set generated better prediction performance than the lateral feature set, while using the total 36-
dimensional feature set generated the best. PCA analysis is helpful in feature space reduction and
performance improvement. For KL grade prediction, the best performance was achieved by ANN with
AUC = 0.761 and F-measure = 0.714. For JSM grade prediction, the best performance was achieved
by random forest with AUC = 0.785 and F-measure = 0.743, while for JSL grade prediction, the best
performance was achieved by the ANN with AUC = 0.695 and Fmeasure = 0.796. As experiment
results showing that the informative locations on medial compartment provide more distinguishing
features than informative locations on lateral compartment.
EPRO
BIOM - 026
A Novel Method to Predict Knee Osteoarthritis Progression on MRI Using Machine
Learning Methods.
Elysium PRO Titles with Abstracts 2018-19
We propose a novel approach to identify one of the most significant dermoscopic criteria in the
diagnosis of cutaneous Melanoma: the blue-whitish structure (BWS). In this paper, we achieve this
goal in a Multiple Instance Learning (MIL) framework using only image-level labels indicating
whether the feature is present or not. To this aim, each image is represented as a bag of (non-
overlapping) regions where each region may or may not be identified as an instance of BWS. A
probabilistic graphical model [1] is trained (in MIL fashion) to predict the bag (image) labels. As
output, we predict the classification label for the image (i.e., the presence or absence of BWS in each
image) and as well we localize the feature in the image. Experiments are conducted on a challenging
dataset with results outperforming state-of-the-art techniques, with BWS detection besting competing
methods in terms of performance. This study provides an improvement on the scope of modelling for
computerized image analysis of skin lesions. In particular, it propounds a framework for identification
of dermoscopic local features from weakly-labelled data.
EPRO
BIOM - 027
Learning to Detect Blue-white Structures in Dermoscopy Images with Weak
Supervision.
Fixed-pattern noise seriously affects the clinical application of optical coherence tomography (OCT),
especially, in the imaging of tumorous tissue. We propose a Hough transform-based fixed-pattern noise
reduction (HTFPNR) method to reduce the fixed-pattern noise for optimizing imaging of tumorous
tissue with OCT system. Using by the HTFPNR method, we detect and map the outline of fixed-pattern
noise in the OCT images, and finally efficiently reduce the fixed-pattern noise by the longitudinal and
horizontal intelligent processing procedure. We adopt the image-to-noise ratio with full information
(INRfi) and the noise reduction ratio (NRR) to evaluate the outcome of fixed-pattern noise reduction
ratio, respectively. The INRfi of OCT image’s noise reduction of ex vivo brainstem tumor is
approximate 21.92 dB. Six groups of OCT images including three types of fixed-pattern noises have
been validated via experimental evaluation of the ex vivo gastric tumor. In the different types of fixed-
pattern noise, the mean INRfis are 25.24 dB, 23.04 dB and 19.35 dB, respectively. This result
demonstrates that it is highly efficient and useful in fixed-pattern noise reduction. The fluctuating range
of the NRR is 0.84-0.88 for three types of added noise in the OCT images. This result demonstrates
that the HTFPNR method can as possible as save useful information by comparing to previous research.
This proposed HTFPNR method can be used into the fixed-pattern noise reduction of OCT images in
other soft biological tissue in the future.
EPRO
BIOM - 028
Optimized Optical Coherence Tomography Imaging with Hough Transform-based
Fixed-pattern Noise Reduction.
Elysium PRO Titles with Abstracts 2018-19
The glomerular filtration rate (GFR) is a crucial index to measure renal function. In daily clinical
practice, the GFR can be estimated using the Gates method, which requires the clinicians to define the
region of interest (ROI) for the kidney and the corresponding background in dynamic renal
scintigraphy. The manual placement of ROIs to estimate the GFR is subjective and labor-intensive,
however, making it an undesirable and unreliable process. This work presents a fully automated ROI
detection method to achieve accurate and robust GFR estimations. After image preprocessing, the ROI
for each kidney was delineated using a shape prior constrained level set (spLS) algorithm and then the
corresponding background ROIs were obtained according to the defined kidney ROIs. In computer
simulations, the spLS method had the best performance in kidney ROI detection compared with the
previous threshold method (Threshold) and the Chan-Vese level set (cvLS) method. In further clinical
applications, 223 sets of 99mTc-diethylenetriaminepentaacetic acid (99mTc-DTPA) renal
scintigraphic images from patients with abnormal renal function were reviewed. Compared with the
former ROI detection methods (Threshold and cvLS), the GFR estimations based on the ROIs derived
by the spLS method had the highest consistency and correlations (r=0.98, p<0.001) with the reference
estimated by experienced physicians.
EPRO
BIOM - 029
Automated Region of Interest Detection Method in Scintigraphic Glomerular
Filtration Rate Estimation.
In recent years, retinal vessel segmentation technology has become an important component for disease
screening and diagnosing in clinical medicine. However, retinal vessel segmentation is a challenging
task due to complex distribution of blood vessels, relatively low contrast between target and
background, and potential presence of illumination and pathologies. In this paper, we propose an
automatic retinal vessel segmentation network using deep supervision and smoothness regularization,
which integrates holistically-nested edge detector (HED) and global smoothness regularization from
conditional random ?elds (CRFs). It is an end-to-end and pixel-to-pixel deep convolutional network,
can perform better results than HED-based methods and the methods where CRF inference is applied
as a post-processing method. With co-constraints between pixels, the proposed DSSRN obtains better
results. Finally, we show that our proposed method obtains a sate-of-the-art vessel segmentation
performance on all three benchmarks, DRIVE, STARE and CHASE DB1.
EPRO
BIOM - 030
Automatic Retinal Vessel Segmentation via Deeply Supervised and Smoothly
Regularized Network.
Elysium PRO Titles with Abstracts 2018-19
In this article, a hybrid image denoising algorithm based on directional diffusion is proposed.
Specifically, we developed a new noise-removal model by combining the modified isotropic diffusion
(ID) model and the modified Perona-Malik (PM) model. The novel hybrid model can adapt the
diffusion process along the tangential direction of edges in the original image via a new control function
based on the patch similarity modulus. In addition, the patch similarity modulus is used as the new
structure indicator for the modified Perona-Malik model. The feature of second order directional
derivative of edge’s tangential direction allows the proposed model to reduce the aliasing and the noise
around edge during edge preserving smoothing. The proposed method is thus able to efficiently
preserve the edges, textures, thin lines, weak edges and fine details, meanwhile preventing the staircase
effects. Computer experiments on synthetic image and nature images demonstrate that the proposed
model achieves a better performance than the conventional partial differential equations (PDEs) models
and some recent advanced models.
EPRO
BIOM - 031
A Hybrid Model for Image Denoising Combining Modified Isotropic Diffusion
Model and Modified Perona-Malik Model.
Predicting malignant potential is one of the most critical components of a computer-aided diagnosis
(CAD) system for gastrointestinal stromal tumors (GISTs). These tumors have been studied only on
the basis of subjective computed tomography (CT) findings. Among various methodologies, radiomics
and deep learning algorithms, specifically convolutional neural networks (CNNs), have recently been
confirmed to achieve significant success by outperforming the state-of-the-art performances in medical
image pattern classification and have rapidly become leading methodologies in this field. However,
the existing methods generally use radiomics or deep convolutional features independently for pattern
classification, which tend to take into account only global or local features, respectively. In this paper,
we introduce and evaluate a hybrid structure that includes different features selected with radiomics
model and CNN and integrates these features to deal with GIST classification. Radiomics model and
CNN architecture are constructed for global radiomics and local convolutional feature selections,
respectively. Subsequently, we utilize distinct radiomics and deep convolutional features to perform
pattern classification for GIST. Specifically, we propose a new pooling strategy to assemble the deep
convolutional features of 54 3D patches from the same case and integrate these features with the
radiomics features for independent case, followed by random forests (RF) classifier. Our method can
be extensively evaluated using multiple clinical datasets.
EPRO
BIOM - 032
Pattern Classification for Gastrointestinal Stromal Tumors by Integration of
Radiomics and Deep Convolutional Features.
Elysium PRO Titles with Abstracts 2018-19
Lesion segmentation is the first step in most automatic melanoma recognition systems. Deficiencies
and difficulties in dermoscopic images such as color inconstancy, hair occlusion, dark corners and
color charts make lesion segmentation an intricate task. In order to detect the lesion in the presence of
these problems, we propose a supervised saliency detection method tailored for dermoscopic images
based on the discriminative regional feature integration (DRFI). DRFI method incorporates multi-level
segmentation, regional contrast, property, background descriptors, and a random forest regressor to
create saliency scores for each region in the image. In our improved saliency detection method, mDRFI,
we have added some new features to regional property descriptors. Also, in order to achieve more
robust regional background descriptors, a thresholding algorithm is proposed to obtain a new pseudo-
background region. Findings reveal that mDRFI is superior to DRFI in detecting the lesion as the
salient object in dermoscopic images. The proposed overall lesion segmentation framework uses
detected saliency map to construct an initial mask of the lesion through thresholding and post-
processing operations. The initial mask is then evolving in a level set framework to fit better on the
lesion's boundaries. The results of evaluation tests on three public datasets show that our proposed
segmentation method outperforms the other conventional state-of-the-art segmentation.
EPRO
BIOM - 033
Supervised Saliency Map Driven Segmentation of Lesions in Dermoscopic Images.
Retinal fundus photographs have been used in the diagnosis of many ocular diseases such as glaucoma,
pathological myopia, age-related macular degeneration and diabetic retinopathy. With the development
of computer science, computer aided diagnosis has been developed to process and analyse the retinal
images automatically. One of the challenges in the analysis is that the quality of the retinal image is
often degraded. For example, a cataract in human lens will attenuate the retinal image, just as a cloudy
camera lens which reduces the quality of a photograph. It often obscures the details in the retinal images
and posts challenges in retinal image processing and analysing tasks. In this paper, we approximate the
degradation of the retinal images as a combination of human-lens attenuation and scattering. A novel
structure-preserving guided retinal image filtering (SGRIF) is then proposed to restore images based
on the attenuation and scattering model. The proposed SGRIF consists of a step of global structure
transferring and a step of global edge-preserving smoothing. Our results show that the proposed SGRIF
method is able to improve the contrast of retinal images, measured by histogram flatness measure,
histogram spread and variability of local luminosity. In addition, we further explored the benefits of
SGRIF for subsequent retinal image processing and analysing tasks. In the two applications of deep
learning based optic cup segmentation and sparse learning based cup-to-disc ratio (CDR) computation
EPRO
BIOM - 034
Structure-preserving Guided Retinal Image Filtering and Its Application for Optic
Disc Analysis.
Elysium PRO Titles with Abstracts 2018-19
Glaucoma is a chronic eye disease that leads to irreversible vision loss. Most of the existing automatic
screening methods firstly segment the main structure, and subsequently calculate the clinical
measurement for detection and screening of glaucoma. However, these measurement-based methods
rely heavily on the segmentation accuracy, and ignore various visual features. In this paper, we
introduce a deep learning technique to gain additional image-relevant information, and screen
glaucoma from the fundus image directly. Specifically, a novel Disc-aware Ensemble Network
(DENet) for automatic glaucoma screening is proposed, which integrates the deep hierarchical context
of the global fundus image and the local optic disc region. Four deep streams on different levels and
modules are respectively considered as global image stream, segmentation-guided network, local disc
region stream, and disc polar transformation stream. Finally, the output probabilities of different
streams are fused as the final screening result. The experiments on two glaucoma datasets (SCES and
new SINDI datasets) show our method outperforms other state-of-the-art algorithms.
EPRO
BIOM - 035
Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image.
Recent studies show that pulmonary vascular diseases may specifically affect arteries or veins through
different physiologic mechanisms. To detect changes in the two vascular trees, physicians manually
analyze the chest computed tomography (CT) image of the patients in search of abnormalities. This
process is time-consuming, difficult to standardize and thus not feasible for large clinical studies or
useful in real-world clinical decision making. Therefore, automatic separation of arteries and veins in
CT images is becoming of great interest, as it may help physicians accurately diagnose pathological
conditions. In this work, we present a novel, fully automatic approach to classifying vessels from chest
CT images into arteries and veins. The algorithm follows three main steps: first, a scale-space particles
segmentation to isolate vessels; then a 3D convolutional neural network (CNN) to obtain a first
classification of vessels; finally, graph-cuts (GC) optimization to refine the results. To justify the usage
of the proposed CNN architecture, we compared different 2D and 3D CNNs that may use local
information from bronchus- and vessel-enhanced images provided to the network with different
strategies. We also compared the proposed CNN approach with a Random Forests (RF) classifier. The
methodology was trained and evaluated on the superior and inferior lobes of the right lung of eighteen
clinical cases with non-contrast chest CT scans, in comparison with manual classification.
EPRO
BIOM - 036
Pulmonary Artery-Vein Classification in CT Images Using Deep Learning.
Elysium PRO Titles with Abstracts 2018-19
Increasing the image quality of positron emission tomography (PET) is an essential topic in the PET
community. For instance, thin pixelated crystals have been used to provide high spatial resolution
images but at the cost of sensitivity and manufacture expense. In this study, we proposed an approach
to enhance the PET image resolution and noise property for PET scanners with large pixelated crystals.
To address the problem of coarse blurred sinograms with large parallax errors associated with large
crystals, we developed a data-driven, single-image super-resolution (SISR) method for sinograms,
based on the novel deep residual convolutional neural network (CNN). Unlike the CNN-based SISR
on natural images, periodically padded sinogram data and dedicated network architecture were used to
make it more efficient for PET imaging. Moreover, we included the transfer learning scheme in the
approach to process cases with poor labeling and small training data set. The approach was validated
via analytically simulated data (with and without noise), Monte Carlo simulated data, and pre-clinical
data. Using the proposed method, we could achieve comparable image resolution and better noise
property with large crystals of bin sizes 4 times of thin crystals with a bin size from 1×1 mm2 to 1.6×1.6
mm2. Our approach uses external PET data as the prior knowledge for training and does not require
additional information during inference.
EPRO
BIOM - 037
Enhancing the image quality via transferred deep residual learning of coarse PET
sonograms.
The Gabor filter (GF) has been proved to show good spatial frequency and position selectivity, which
makes it a very suitable solution for visual search, object recognition, and, in general, multimedia
processing applications. GFs prove useful also in the processing of medical imaging to improve part
of the several filtering operations for their enhancement, denoising, and mitigation of artifact issues.
However, the good performances of GFs are compensated by a hardware complexity that traduces in
a large amount of mapped physical resources. This paper presents three different designs of a GF,
showing different tradeoffs between accuracy, area, power, and timing. From the comparative study,
it is possible to highlight the strength points of each one and choose the best design. The designs have
been targeted to a Xilinx field-programmable gate array (FPGA) platform and synthesized to 90-nm
CMOS standard cells. FPGA implementations achieve a maximum operating frequency among the
different designs of 179 MHz, while 350 MHz is obtained from CMOS synthesis. Therefore, 86 and
168 full-HD (1920 x 1080) f/s could be processed, with FPGA and std_cell implementations,
respectively. In order to meet space constraints, several considerations are proposed to achieve an
optimization in terms of power consumption, while still ensuring real-time performances.
EPRO
BIOM - 038
Design of a Gabor Filter HW Accelerator for Applications in Medical Imaging.
Elysium PRO Titles with Abstracts 2018-19
We investigate the use of deep neural networks (DNNs) for suppressing off-axis scattering in
ultrasound channel data. Our implementation operates in the frequency domain via the short-time
Fourier transform. The inputs to the DNN consisted of the separated real and imaginary components
(i.e. inphase and quadrature components) observed across the aperture of the array, at a single
frequency and for a single depth. Different networks were trained for different frequencies. The output
had the same structure as the input and the real and imaginary components were combined as complex
data before an inverse short-time Fourier transform was used to reconstruct channel data. Using
simulation, physical phantom experiment, and in vivo scans from a human liver, we compared this
DNN approach to standard delay-and-sum (DAS) beamforming and an adaptive imaging technique
that uses the coherence factor (CF). For a simulated point target, the side lobes when using the DNN
approach were about 60 dB below those of standard DAS. For a simulated anechoic cyst, the DNN
approach improved contrast ratio (CR) and contrast-to-noise (CNR) ratio by 8.8 dB and 0.3 dB,
respectively, compared to DAS. For an anechoic cyst in a physical phantom, the DNN approach
improved CR and CNR by 17.1 dB and 0.7 dB, respectively. For two in vivo scans, the DNN approach
improved CR and CNR by 13.8 dB and 9.7 dB, respectively. We also explored methods for examining
how the networks in this work function.
EPRO
BIOM - 039
Deep Neural Networks for Ultrasound Beamforming.
This paper introduces a computer-aided kidney shape detection method suitable for volumetric (3D)
ultrasound images. Using shape and texture priors, the proposed method automates the process of
kidney detection, which is a problem of great importance in computer-assisted trauma diagnosis. This
paper introduces a new complex-valued implicit shape model which represents the multi-regional
structure of the kidney shape. A spatially aligned neural network classifiers with complex-valued
output is designed to classify voxels into background and multi-regional structure of the kidney shape.
The complex values of the shape model and classification outputs are selected and incorporated in a
new similarity metric such the shape-to-volume registration process only fits the shape model on the
actual kidney shape in input ultrasound volumes. The algorithm's accuracy and sensitivity are evaluated
using both simulated and actual 3D ultrasound images, and it is compared against the performance of
the state-of-the-art. The results support the claims about accuracy and robustness of the proposed
kidney detection method, and statistical analysis validates its superiority over state-of-the-art.
EPRO
BIOM - 040
Kidney Detection in 3D Ultrasound Imagery Via Shape to Volume Registration
Based on Spatially Aligned Neural Network.
Elysium PRO Titles with Abstracts 2018-19
Optical Coherence Tomography (OCT) is becoming one of the most important modalities for the
noninvasive assessment of retinal eye diseases. As the number of acquired OCT volumes increases,
automating the OCT image analysis is becoming increasingly relevant. In this paper, we propose a
surrogate-assisted classification method to classify retinal OCT images automatically based on
convolutional neural networks (CNNs). Image denoising is first performed to reduce the noise.
Thresholding and morphological dilation are applied to extract the masks. The denoised images and
the masks are then employed to generate a lot of surrogate images, which are used to train the CNN
model. Finally, The prediction for a test image is determined by the average of the outputs from the
trained CNN model on the surrogate images. The proposed method has been evaluated on different
databases. The results (AUC of 0.9783 in the local database and AUC of 0.9856 in the Duke database)
show that the proposed method is a very promising tool for classifying the retinal OCT images
automatically.
EPRO
BIOM - 041
Surrogate-assisted Retinal OCT Image Classification Based on Convolutional Neural
Networks.
Acute ischemic stroke is recognized as a common cerebral vascular disease in aging people. Accurate
diagnosis and timely treatment can effectively improve the blood supply of the ischemic area and
reduce the risk of disability or even death. Understanding the location and size of infarcts plays a
critical role in the diagnosis decision. However, manual localization and quantification of stroke lesions
are laborious and timeconsuming. In this paper, we propose a novel automatic method to segment acute
ischemic stroke from diffusion weighted images (DWI) using deep 3D convolutional neural networks
(CNNs). Our method can efficiently utilize 3D contextual information and automatically learn very
discriminative features in an end-to-end and data-driven way. To relieve the difficulty of training very
deep 3D CNN, we equip our network with dense connectivity to enable the unimpeded propagation of
information and gradients throughout the network. We train our model with Dice objective function to
combat the severe class imbalance problem in data. A DWI dataset containing 242 subjects (90 for
training, 62 for validation and 90 for testing) with various types of acute ischemic stroke was
constructed to evaluate our method. Our model achieved high performance on various metrics (Dice
similarity coefficient: 79.13%, lesion-wise precision: 92.67%, lesion-wise F1 score: 89.25%),
outperforming other state-of-the-art CNN methods by a large margin.
EPRO
BIOM - 042
Automatic Segmentation of Acute Ischemic Stroke from DWI using 3D Fully
Convolutional DenseNets.
Elysium PRO Titles with Abstracts 2018-19
Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic
medical image segmentation. However, they have not demonstrated sufficiently accurate and robust
results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the
lack of generalizability to previously unseen object classes (a.k.a. zero-shot learning). To address these
problems, we propose a novel deep learning-based interactive segmentation framework by
incorporating CNNs into a bounding box and scribble-based segmentation pipeline. We propose
image-specific fine-tuning to make a CNN model adaptive to a specific test image, which can be either
unsupervised (without additional user interactions) or supervised (with additional scribbles). We also
propose a weighted loss function considering network and interaction-based uncertainty for the fine-
tuning. We applied this framework to two applications: 2D segmentation of multiple organs from fetal
Magnetic Resonance (MR) slices, where only two types of these organs were annotated for training;
and 3D segmentation of brain tumor core (excluding edema) and whole brain tumor (including edema)
from different MR sequences, where only the tumor core in one MR sequence was annotated for
training.
EPRO
BIOM - 043
Interactive Medical Image Segmentation using Deep Learning with Image-specific
Fine-tuning.
The author introduces a contour detection method that has relatively low complexity yet still highly
accurate. The method is based on extrema detection along the four principal orientations, a trick that
can be used to detect not only edges but, in particular, also ridges and rivers. The author makes a
comparison to the popular Canny algorithm and shows that the proposed method's only downside is
that it cannot detect very high curvatures in edge contours. The method is applied to the task of image
classification (satellite images, Caltech-101, etc.) and it is demonstrated that the use of all three contour
types (edges, ridges, and rives) improves classification accuracy as opposed to the use of only edge
contours. Thus, for image classification, it is more important to extract multiple contour features; the
use of the exact detection method appears to play a smaller role. The author's simple method is also
appealing for use in individual frames, due to its low complexity.
EPRO
BIOM - 044
Rapid contour detection for image classification.
Elysium PRO Titles with Abstracts 2018-19
Glaucoma is a chronic eye disease that leads to irreversible vision loss. The cup to disc ratio (CDR)
plays an important role in the screening and diagnosis of glaucoma. Thus, the accurate and automatic
segmentation of optic disc (OD) and optic cup (OC) from fundus images is a fundamental task. Most
existing methods segment them separately, and rely on hand-crafted visual feature from fundus images.
In this paper, we propose a deep learning architecture, named M-Net, which solves the OD and OC
segmentation jointly in a one-stage multilabel system. The proposed M-Net mainly consists of multi-
scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function. The
multi-scale input layer constructs an image pyramid to achieve multiple level receptive field sizes. The
U-shape convolutional network is employed as the main body network structure to learn the rich
hierarchical representation, while the side-output layer acts as an early classifier that produces a
companion local prediction map for different scale layers. Finally, a multi-label loss function is
proposed to generate the final segmentation map. For improving the segmentation performance further,
we also introduce the polar transformation, which provides the representation of the original image in
the polar coordinate system.
EPRO
BIOM - 045
Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and
Polar Transformation.
Acute ischemic stroke is recognized as a common cerebral vascular disease in aging people. Accurate
diagnosis and timely treatment can effectively improve the blood supply of the ischemic area and
reduce the risk of disability or even death. Understanding the location and size of infarcts plays a
critical role in the diagnosis decision. However, manual localization and quantification of stroke lesions
are laborious and time consuming. In this paper, we propose a novel automatic method to segment
acute ischemic stroke from diffusion weighted images (DWI) using deep 3D convolutional neural
networks (CNNs). Our method can efficiently utilize 3D contextual information and automatically
learn very discriminative features in an end-to-end and data-driven way. To relieve the difficulty of
training very deep 3D CNN, we equip our network with dense connectivity to enable the unimpeded
propagation of information and gradients throughout the network. We train our model with Dice
objective function to combat the severe class imbalance problem in data. A DWI dataset containing
242 subjects (90 for training, 62 for validation and 90 for testing) with various types of acute ischemic
stroke was constructed to evaluate our method. Our model achieved high performance on various
metrics (Dice similarity coefficient: 79.13%, lesion-wise precision: 92.67%, lesion-wise F1 score:
89.25%), outperforming other state-of-the-art CNN methods by a large margin.
EPRO
BIOM - 046
Automatic Segmentation of Acute Ischemic Stroke from DWI using 3D Fully
Convolutional DenseNets.
Elysium PRO Titles with Abstracts 2018-19
Malignant skin lesions are among the most common types of cancer, and automated systems for their
early detection are of fundamental importance. We propose SDI+, an unsupervised algorithm for the
segmentation of skin lesions in dermoscopic images. It is articulated into three steps, aimed at
extracting preliminary information on possible confounding factors, accurately segmenting the lesion,
and post-processing the result. The overall method achieves high accuracy on dark skin lesions and
can handle several cases where confounding factors could inhibit a clear understanding by a human
operator. We present extensive experimental results and comparisons achieved by the SDI+ algorithm
on the ISIC 2017 dataset, highlighting the advantages and disadvantages.
EPRO
BIOM - 047
SDI+: a Novel Algorithm for Segmenting Dermoscopic Images.
A troublesome disease in which damages of the optic nerve of eye's is nothing but the glaucoma, which
causes irretrievable loss of vision. Glaucoma is a disease where if treatment is get late, the person can
blind. Normally glaucoma detects when there is an increase in the fluid in the front of eye. When that
extra fluid is increased, the pressure in your eye is also getting increased. Accordingly, the size of the
optic disc and optic cup is increased as a result diameter also increased. The ratio of the cup and disc
diameter is called cup-to-disc ratio (CDR). Threshold type segmentation method is used in this system
for localizing the optic disc and optic cup. Another edge detection and ellipse fitting algorithm are also
used. The proposed system for optic disc and optic cup localization and CDR calculation is MATLAB
GUI software.
EPRO
BIOM - 048
Glaucoma Detection from Fundus Images Using MATLAB GUI.
Elysium PRO Titles with Abstracts 2018-19
The classification of medical images and illustrations from the biomedical literature is important for
automated literature review, retrieval and mining. Although deep learning is effective for large-scale
image classification, it may not be the optimal choice for this task as there is only a small training
dataset. We propose a combined deep and handcrafted visual feature (CDHVF) based algorithm that
uses features learned by three fine-tuned and pre-trained deep convolutional neural networks (DCNNs)
and two handcrafted descriptors in a joint approach. We evaluated the CDHVF algorithm on the
ImageCLEF 2016 Subfigure Classification dataset and it achieved an accuracy of 85.47%, which is
higher than the best performance of other purely visual approaches listed in the challenge leaderboard.
Our results indicate that handcrafted features complement the image representation learned by DCNNs
on small training datasets and improve accuracy in certain medical image classification problems.
EPRO
BIOM - 049
Classification of Medical Images in the Biomedical Literature by Jointly Using Deep
and Handcrafted Visual Features.
Ultrasound diagnosis is routinely used in obstetrics and gynecology for fetal biometry, and owing to
its time-consuming process, there has been a great demand for automatic estimation. However, the
automated analysis of ultrasound images is complicated because they are patient-specific, operator-
dependent, and machine-specific. Among various types of fetal biometry, the accurate estimation of
abdominal circumference (AC) is especially difficult to perform automatically because the abdomen
has low contrast against surroundings, non-uniform contrast, and irregular shape compared to other
parameters. We propose a method for the automatic estimation of the fetal AC from 2D ultrasound data
through a specially designed convolutional neural network (CNN), which takes account of doctors'
decision process, anatomical structure, and the characteristics of the ultrasound image. The proposed
method uses CNN to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein)
and Hough transformation for measuring AC. We test the proposed method using clinical ultrasound
data acquired from 56 pregnant women. Experimental results show that, with relatively small training
samples, the proposed CNN provides sufficient classification results for AC estimation through the
Hough transformation. The proposed method automatically esti mates AC from ultrasound images.
The method is quantitatively evaluated, and shows stable performance in most cases and even for
ultrasound images deteriorated by shadowing artifacts.
EPRO
BIOM - 050
Automatic Estimation of Fetal Abdominal Circumference from Ultrasound Images.