unsupervised medical image classification by combining case-based classifiers thien anh dinh 1, tomi...

Unsupervised medical image classification by combining

case-based classifiers

Thien Anh Dinh1, Tomi Silander1, Bolan Su1, Tianxia Gong

Boon Chuan Pang2, Tchoyoson Lim2, Cheng Kiang Lee2

Chew Lim Tan1,Tze-Yun Leong1

1National University of Singapore2National Neuroscience Institute

3Bioinformatics Institute, Singapore

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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:

1. To extract knowledge from images to facilitate text-based retrieval of relevant images

2. To provide a second source of opinions for clinicians on abnormality detection and pathology classification

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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

Extracting features

Selecting discriminative features

Building classifiers Labeling

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Objective

• An automated pathology classification system for volumetric brain image slices

• Main highlights

1. Eliminates the need for segmentation and semantic or annotation-based feature selection

• Reduces the amount of manual work for constructing an annotation system

2. Extracts automatically and efficiently knowledge from images

3. Improves the utilization of medical image databases

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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

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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

1. Reconstructing: a test image as a linear combination of a small number of training images

2. Classifying: evaluating how the images belonging to different classes contribute to the reconstruction of the test image

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Sparse representation-based classifier(Wright et al.)

y ≈ a7x7 + a23x23 + a172x172 + a134x134 + a903x903

Image databases x1, x2,…, x1000

Sparsereconstruction

Classresiduals

New data item

y ≈ a7x7 + a23x23 + a172x172 + a134x134 + a903x903

r1 = || y – (a7x7 + a172x172 + a132x134)||2

r2 = || y – (a23x23 + a903x903)||2

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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

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Domain

• Automatically annotate CT brain images for traumatic brain injury (TBI)

TBI: major cause of death and disability

• Several types of hemorrhages:

i. Extradural hematoma (EDH)

ii. Subdural hematoma (SDH)

iii. Intracerebral hemorrage (ICH)

iv. Subarachnoid hemorrhage (SAH)

v. Intraventricular hematoma (IVH) Subdural hematomaExtradural hematoma

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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

SDH57%ED

H23%

ICH21%

12A volumetric CT brain scan with 19 slices

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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

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Experimental results

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Experimental results when varying the ensemble size

Average precision and recall of classifiers when varying the ensemble size (number of features = 1000)

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Experimental results when varying the number of features per classifier

Average precisions and recalls of classifiers when varying number of features (ensemble size = 50)

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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

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THANK YOU

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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 grayscale image: 655360 features

Randomly select 4000 Gabor features to form a feature subspace