Computational Image

ClassificationUMBC Department of Computer Science

eBiquity Research GroupFebruary 19, 2010

Overview

Introductions Image Classification Initial Results Future Efforts

Introductions Faculty

Yelena Yesha, PhD Michael Grasso, MD, PhD John Dorband, PhD Tim Finin, PhD Milt Halem, PhD Anupam Joshi, PhD

Graduate students Ronil Mokashi Darshana Dalvi

Computational Image Classification

Categorize a raster image into a finite set of classes. Convert raster data into feature

vectors. Support vector machine image

classifier. Metadata to map specific classes to

biological characteristics.

Image Classification Examples

Computer assisted diagnosis of prostate and breast cancer biopsies.

Segmentation of hysteroscopy video. Echocardiogram analysis. Skin cancer detection.

Biomedical Imaging: from Nano to Macro, 2007;:1284-1287 IEEE TITB, 2008 May;12(3):366-376

Proceedings 27th IEEE EMBS, 2005;:5680-5683Conf Proc IEEE Eng Med Biol Soc, 2006;1:4775-8

Related Efforts: Video Segmentation

Laparoscopic cholecystectomy videos. 378 representative images from 5

videos. Analyzed 49 separate image features.

Related Efforts: Video Segmentation

Image classification. Distance metric to identify best

features. Support vector machine image

classifier. Accuracy of 91%.Video

Segments

Frames

Future directions. Real-time analysis to assess patient

safety. Time and motion analysis of surgical

instruments. Classification of pathology.

Hiatal hernias. Capsule endoscopy.

Related Efforts: Video Segmentation

Related Efforts: Cancer Screening

Skin caner screening. Handheld iPhone image classifier. Tool for primary care physicians. Identify lesions in

need of dermatology referral.

NIH and UMB proposals pending.

Image Classification Approach

Feature ExtractionFeature Models

Model Features

Image Classifier

Images Organized by Class

Unknown Image

Feature Extraction

Image Features

Spectral features (color/tone). Histogram (3D, color, binary, gray). Distribution, size, width, mean, stdev. Do not vary with translation and rotation.

Textural features (spatial distribution). Gray-level co-occurrence matrix (GLCM). Energy, entropy, contrast, correlation. Independent of color distribution.

IEEE Transaction on Systems, Man, and Cybernetics. 1973 Nov; 3(6):610-621

Advanced Features - Context Image segmentation.

Regions of interest (contextual features). Threshold algorithms - Maximum

Entropy, Otsu Threshold, Watershed, etc. Segmentation features of actin fibers.

Density - Area, Mean Gray Level. Distribution - Centroid, Center of Mass. Orientation - Angle, Elliptical Fit (wrt cell). Order - Angle, Elliptical Fit (wrt fibers).

Advanced Features - Clustering K-means clustering of image

features. Partitions images into clusters based

on the nearest mean, based on a first-order Markov property.

Based on the assumption that images with similar clinical features are more likely to be found in the same cluster.

Model Development

Distance metrics Manhattan distance, Jeffrey divergence. Classification threshold.

Support vector machines Machine learning methods. An N-dimensional hyperplane optimally

separates images into categories. This mapping is performed by a set of

mathematical functions, known as kernels.

Initial Results - Feature Analysis

Initial image classification experiment. Evaluated 15 spectral and textural features. Total of 11 images in 4 groups.

Focal adhesions images, actin stained. 1hdry, 1hwet, 24hdry, 24hwet.

Analysis. Leave-one-out technique over all 11 images. Manhattan distance. Threshold of 5% (0.3% for histograms).

Initial Results - Feature Analysis

Trait+

Trait-

Feature+

43 17 60

Feature-

13 147 160

56 164 220

Promising features. Gray-scale

distribution Medium Mode Homogeneity Energy Entropy Inverse difference

moment

Sensitivity = 76.8%Specificity = 89.6%Accuracy = 86.4%

Initial Results - Segmentation

Second image classification experiment. Evaluating 4 new image features.

Density, Distribution, Orientation, Order Experimenting with threshold algorithms

to optimize image segmentation. Total of 11 images in 4 groups.

Focal adhesions images, actin stained. 1hdry, 1hwet, 24hdry, 24hwet.

Initial Results - Segmentation

Orientation feature using elliptical fit. Image moment-preserving threshold. Elliptical fit. Cell angle = 132° Average (weighted) actin fiber angle = 129°.

Initial Results - Segmentation

Initial Results - Analysis Platform

To automate and optimize image processing algorithms.

Future Efforts Use feature analysis to develop a support

vector machine image classifier. Continue image segmentation work. Correlate actin data to other images. Incorporate successful algorithms in the

Analysis Platform. Identify ontologies to map specific

classes to biological characteristics.