Abstract
Image segmentation applied to medical image analysis is still a critical and important task. Although there exist several segmentation algorithms that have been widely studied in literature, these are subject to segmentation problems such as over- and under-segmentation as well as non-closed edges. In this paper, a simple method that combines well-known segmentation algorithms is presented. This method is applied to detect acid-fast bacilli (AFB) in bacilloscopies used to diagnose pulmonary tuberculosis (TB). This diagnosis can be performed through different tests, and the most used worldwide is smear microscopy because of its low cost and effectiveness. This diagnosis technique is based on the analysis and counting of the bacilli in the bacilloscopy observed under an optical microscope. The proposed method is used to segment the bacilli in digital images from bacilloscopies processed using Ziehl-Neelsen (ZN) staining. The proposed method is fast, has a low computational cost and good efficiency compared to other methods. The bacilli image segmentation is performed by image processing and analysis techniques, probability concepts and classifiers. In this work, a Bayesian classifier based on a Gaussian mixture model (GMM) is used. The segmentations' results are validated by using the Jaccard index, which indicates the efficiency of the classifier.
Highlights
As indicated by Balandrano et al in [1] the tuberculosis is considered one of the most important reemerging diseases and a public health problem worldwide, aggravated by the HIV epidemic and the drug resistance to tuberculosis
This paper focuses on the ZN staining technique because it is the most used worldwide and does not require special equipment; it is recommended by the World Health Organization (WHO) and by the International Union against Tuberculosis and Lung Disease (IUATLD)
The main objective of this article is the implementation of a simple methodology that utilizes a Bayesian classifier based on a Gaussian mixture model (GMM) to segment tuberculosis bacilli, while obtaining a high detection effectiveness, as well as decreasing the processing time and the computational cost
Summary
As indicated by Balandrano et al in [1] the tuberculosis is considered one of the most important reemerging diseases and a public health problem worldwide, aggravated by the HIV epidemic and the drug resistance to tuberculosis. The World Health Organization (WHO) [2] estimates that one fourth of the world’s population has latent tuberculosis. Tuberculosis is a chronic, infectious and curable disease, which is caused in 95% of the cases by the bacillus Mycobacterium tuberculosis (M. tuberculosis) or Koch’s bacillus. Image processing for AFB segmentation for pulmonary tuberculosis diagnosis
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