Automatic Identification of Mycobacterium tuberculosis in Ziehl-Neelsen Stained Sputum Smear Microscopy Images using a Two-stage Classifier

Abstract

This paper presents a method for the automatic identification of Mycobacterium tuberculosis in Ziehl-Neelsen stained sputum smear microscopy images, the most common bacilloscopy method in developing countries due to its low costs. The proposed method is divided in two stages: a projection of the original coloured image followed by the segmentation and the elimination of large and small segmented structures, and the classification of structures using neural networks and support vector machines. The segmentation of structures presents a loss of bacilli of 1.31 %, while the elimination of areas increases the loss to 14.39 %. The evaluation of the classification of structures is made using cross validation and a maximum sensitivity of 94.25 % is obtained. The presented method has a low computational cost, allying performance and efficiency.