Texture Analysis from 3D Model and Individual Slice Extraction for Tuberculosis MDR Detection, Type Classification and Severity Scoring

Tuberculosis (TB) is a dreaded bacterial infection that affects human lungs. It has been known to mankind since ancient ages. Tuberculosis ImageCLEF 2018 proposes a set of tasks based on Computed Tomography (CT) scan images of patients’ lungs. They are: multi-drug resistance (MDR) detection, tuberculosis type (TBT) classification and severity scoring (SVR). In this work, two different methods are presented to solve these problems. Texture analysis based methods (3D Modeling and Slice extraction approach) were used to generate feature values from CT scans and different classifiers were tested. 3D Modeling approach calculates seven statistical features of Mean, Skewness, Kurtosis, Homogeneity, Energy, Entropy and Fractal Dimension. And Slice extraction approach calculates 96 dimensional feature vector based on Contrast, Correlation, Energy, Homogeneity, Entropy and Mean. In accordance with the ranking given by the organizers, this approach was ranked 1 for multi-drug resistance detection, 5 for tuberculosis type classification and 3 tuberculosis severity scoring.

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