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Thien Anh Dinh 1 , Tomi Silander 1 , Bolan Su 1 , Tianxia Gong Boon Chuan Pang 2 , Tchoyoson Lim 2 , Cheng Kiang Lee 2 Chew Lim Tan 1 ,Tze-Yun Leong 1 1 National University of Singapore 2 National Neuroscience Institute 3 Bioinformatics Institute, Singapore.
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Thien Anh Dinh1, TomiSilander1, Bolan Su1, Tianxia Gong Boon Chuan Pang2, Tchoyoson Lim2, Cheng Kiang Lee2 Chew Lim Tan1,Tze-Yun Leong1 1National University of Singapore 2National Neuroscience Institute 3Bioinformatics Institute, Singapore Unsupervised medical image classification by combining case-based classifiers
Automated medical image annotation • Huge amount of valuable data available in medical image databases • Not fully utilized for medical treatment, research and education • Medical image annotation: • To extract knowledge from images to facilitate text-based retrieval of relevant images • To provide a second source of opinions for clinicians on abnormality detection and pathology classification
Problem • Flowchart of current methods • Challenges in current methods • Highly sensitive and accurate segmentation • Extracting domain knowledge • Automatic feature selection • Time-consuming manual adjustment process • reduces usages of medical image annotation systems
Objective • An automated pathology classification system for volumetric brain image slices • Main highlights • Eliminates the need for segmentation and semantic or annotation-based feature selection • Reduces the amount of manual work for constructing an annotation system • Extracts automatically and efficiently knowledge from images • Improves the utilization of medical image databases
System overview • Case-based classifier • Gabor filters • Non domain specific features • Localized low-level features • Ensemble learning • Set of classifiers • Each classifier with a random subset of features • Final classification: an aggregated result
Sparse representation-based classifier • Sparse representation-based classifier (SRC) proposed by Wright et al. for face recognition task • Non-parametric sparse representation classifier • SRC consists of two stages • Reconstructing: a test image as a linear combination of a small number of training images • Classifying: evaluating how the images belonging to different classes contribute to the reconstruction of the test image
Image databases x1, x2,…, x1000 Sparse reconstruction New data item y ≈ a7x7 + a23x23 + a172x172 + a134x134 + a903x903 y ≈ a7x7 + a23x23 + a172x172 + a134x134 + a903x903 Class residuals r1 = || y – (a7x7 + a172x172 + a132x134)||2 r2 = || y – (a23x23 + a903x903)||2
Ensemble of weak classifiers • Combine multiple weak classifiers • Take class specific residuals as confidence measures • The smaller the residual for the class, the better we construct the test by just using the samples from that class • To classify image y, compute average class-specific residuals of all W weak classifiers
Domain • Automatically annotate CT brain images for traumatic brain injury (TBI) • TBI: major cause of death and disability • Several types of hemorrhages: • Extradural hematoma (EDH) • Subdural hematoma (SDH) • Intracerebralhemorrage (ICH) • Subarachnoid hemorrhage (SAH) • Intraventricular hematoma (IVH) Extradural hematoma Subdural hematoma
Data • CT brain scans of 103 patients • Each scan: • Volumetric stack of 18-30 images (slices) • Image resolution: 512 x 512 pixels • Manually assigned a hematoma type extracted from its medical text report
Experimental setup • Compared performances of • SRC vs. SVM vs. SVM + feature selection • With/without ensemble learning • Run stratified ten-fold cross-validation 50 times with different random foldings • Measured the average precisions and recalls • Separated training and testing dataset at the case level
Experimental results when varying the ensemble size Average precision and recall of classifiers when varying the ensemble size (number of features = 1000)
Experimental results when varying the number of features per classifier Average precisions and recalls of classifiers when varying number of features (ensemble size = 50)
Conclusion • Ensemble classification framework with sparse Gabor-feature based classifier • Eliminates the requirement for segmentation and supervised feature selection • Reduces the need for manual adjustment • Achieves reasonable results compared to segmentation dependent techniques (Gong et al.) • Limitation • Longer classification time when dealing with large training data • Manual weighting needed for imbalanced data
Gabor features • Localize low level features from an input image • Resemble the primitive features extracted by human visual cortex • Extract edge like features in different scales and orientations at different locations of the image • Create a Gabor filter bank with 5 frequencies and 8 orientations • A 128 x 128 grayscaleimage: 655360 features • Randomly select 4000 Gabor features to form a feature subspace