Automated detection of tuberculosis on sputum smeared slides using stepwise classification

Ajay Divekar; Corina Pangilinan; Gerrit Coetzee; Tarlochan Sondh; Fleming Y. M. Lure; Sean Kennedy

doi:10.1117/12.910484

23 February 2012 Automated detection of tuberculosis on sputum smeared slides using stepwise classification

Ajay Divekar, Corina Pangilinan, Gerrit Coetzee, Tarlochan Sondh, Fleming Y. M. Lure, Sean Kennedy

Proceedings Volume 8315, Medical Imaging 2012: Computer-Aided Diagnosis; 83153E (2012) https://doi.org/10.1117/12.910484
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Routine visual slide screening for identification of tuberculosis (TB) bacilli in stained sputum slides under microscope system is a tedious labor-intensive task and can miss up to 50% of TB. Based on the Shannon cofactor expansion on Boolean function for classification, a stepwise classification (SWC) algorithm is developed to remove different types of false positives, one type at a time, and to increase the detection of TB bacilli at different concentrations. Both bacilli and non-bacilli objects are first analyzed and classified into several different categories including scanty positive, high concentration positive, and several non-bacilli categories: small bright objects, beaded, dim elongated objects, etc. The morphological and contrast features are extracted based on aprior clinical knowledge. The SWC is composed of several individual classifiers. Individual classifier to increase the bacilli counts utilizes an adaptive algorithm based on a microbiologist's statistical heuristic decision process. Individual classifier to reduce false positive is developed through minimization from a binary decision tree to classify different types of true and false positive based on feature vectors. Finally, the detection algorithm is was tested on 102 independent confirmed negative and 74 positive cases. A multi-class task analysis shows high accordance rate for negative, scanty, and high-concentration as 88.24%, 56.00%, and 97.96%, respectively. A binary-class task analysis using a receiver operating characteristics method with the area under the curve (Az) is also utilized to analyze the performance of this detection algorithm, showing the superior detection performance on the high-concentration cases (Az=0.913) and cases mixed with high-concentration and scanty cases (Az=0.878).

Citation Download Citation

Ajay Divekar, Corina Pangilinan, Gerrit Coetzee, Tarlochan Sondh, Fleming Y. M. Lure, and Sean Kennedy "Automated detection of tuberculosis on sputum smeared slides using stepwise classification", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83153E (23 February 2012); https://doi.org/10.1117/12.910484

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