HEP-2 CELLS CLASSIFICATION VIA FUSION OF MORPHOLOGICAL AND TEXTURAL FEATURES 13 November 2012 IEEE...
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Transcript of HEP-2 CELLS CLASSIFICATION VIA FUSION OF MORPHOLOGICAL AND TEXTURAL FEATURES 13 November 2012 IEEE...
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HEP-2 CELLS CLASSIFICATION VIA FUSION OF MORPHOLOGICAL
AND TEXTURAL FEATURES
13 November 2012IEEE 12th International Conference on BioInformatics & BioEngineering
I. Theodorakopoulos, D. Kastaniotis, G. Economou and S. Fotopoulos
{iltheodorako, dkastaniotis}@upatras.gr , {economou, spiros}@physics.upatras.gr
Computer Vision GroupElectronics LaboratoryPhysics Department
University of Patras, Greece
www.upcv.upatras.gr www.ellab.physics.upatras.gr
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Motivation
The standard screening test for detection of autoimmune diseases is the indirect immunofluorescence (IIF) test.
Human autoantibodies (AABs) associated with various autoimmune conditions, are detected by specific fluorescence patterns on a human epithelial cell line (HEp-2).
Testing is performed manually but : Requires highly-specialized personnel. Time-consuming procedure. Low standardization leads to high inter-laboratory
variance.
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Typical IIF Procedure
Image Acquisition
Image Segmentation
Mitosis Detection
Fluorescence Intensity
Classification
Staining Pattern Recognition
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Typical IIF Procedure
Image Acquisition
Image Segmentation
Mitosis Detection
Fluorescence Intensity
Classification
Staining Pattern Recognition
Input: Single-Cell Images Cell Contour Fluorescence
Intensity
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Taxonomy
More than thirty different nuclear and cytoplasm patterns could be identified.
Can be grouped into six basic patterns:
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Properties
Different staining patterns present variations both in: Morphological characteristics
Shape complexity Holes Intensity peaks
Textural characteristics Smooth Areas Grainy Areas
In order to capture the unique properties of each pattern, incorporation of both morphological and textural descriptors seems reasonable.
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Multi-level Thresholding
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Multi-level Thresholding
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Multi-level Thresholding
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Multi-level Thresholding
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Multi-level Thresholding
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Morphological Features
Cell’s contour complexity Threshold cell image into 9 levels equally spaced
between intensity extremes. Perform Connected Components Analysis on each
binary image. Discard blobs with area <1% of the mean area. On each binary image, compute:
Number of detected blobs Density of detected blobs Mean solidity of the detected blobs
Concatenate all features to a 28-dimensional descriptor
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Local Binary Patterns (LBPs)
88 102
133
14 100
200
40 110 92
0 1 1 1 0 1 0 0
0 1 1
0 1
0 1 0
116
A well-established textural descriptor.
The biggest part of textural information is encoded in the 58 uniform patterns.
LBPs are not rotation invariant. A simple solution is to calculate the uniform
LPBs histograms on 80 rotated instances of the cell image (4.5 deg intervals).
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Local Binary Patterns (LBPs)
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Classification
The 28 morphological features and the 58-bin LBPs descriptor are concatenated in a 86-Dimensional feature vector.
Classification is performed using non-linear SVMs with Gaussian kernel.
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Evaluation
Lack of publicly available datasets.
Evaluation on the dataset of HEp-2 cell classification contest (hosted by ICPR 2012 conference) 721 single-cell fluorescence images. Manually segmented and annotated by specialists in order to
provide ground truth. Binary masks and fluorescence intensity are provided. Approximately uniform distribution of patterns.
There are not reported results for comparison yet.
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Results
K-fold validation - Classification performance of the various feature
sets for variable k
Confusion Matrix for 10-fold validation procedure using morphological and
textural features’ fusion
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Thank You
This research has been co-financed by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: Heracleitus II. Investing in knowledge society through the European Social Fund.
Acknowledgment