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Erwin Dianderas
Universidad Nacional de Ingeniería
Computer Science & IT Electrical & Electronics Engineering

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A novel algorithm for detection of tuberculosis bacilli in sputum smear fluorescence images Erwin Dianderas; Christian del Carpio; Mirko Zimic; Patricia Sheen; Jorge Coronel; Roberto Lavarello; Guillermo Kemper
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (773.647 KB) | DOI: 10.11591/ijece.v10i6.pp5665-5677

Abstract

This work proposes an algorithm aimed at recognizing and accounting Koch bacilli in digital images of microbiological sputum samples stained with auramine, in order to determine the degree of concentration and the state of the disease (tuberculosis). The algorithm was developed with the main objective of maximizing the sensitivity and specificity of the analysis of microbiological samples (recognition and counting of bacilli) according to each preparation method (direct and diluted pellets) in order to reduce the subjectivity of the visual inspection applied by the specialist at the time of analyzing the samples. The proposed algorithm consists of a background removal, an image improvement stage based on consecutive morphological closing operations, a segmentation stage of objects of interest based on thresholdization and a classification stage based on SVM. Each algorithmic stage was developed taking into account the method of preparation of the sample to be processed, being this aspect the main contribution of the proposed work, since it was possible to achieve very satisfactory results in terms of specificity and sensitivity. In this context, sensitivity levels of 91.24% and 93.79% were obtained. Specificity levels of 90.33% and 94.85% were also achieved for direct and diluted pellet methods respectively.
An algorithm for detection of tuberculosis bacilli in Ziehl-Neelsen sputum smear images Christian del Carpio; Erwin Dianderas; Mirko Zimic; Patricia Sheen; Jorge Coronel; Roberto Lavarello; Guillermo Kemper
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1338.919 KB) | DOI: 10.11591/ijece.v9i4.pp2968-2981

Abstract

This work proposes an algorithm oriented to the detection of tuberculosis bacilli in digital images of sputum samples, inked with the Ziehl Neelsen method and prepared with the direct, pellet and diluted pellet methods. The algorithm aims at automating the optical analysis of bacilli count and the calculation of the concentration level. Several algorithms have been proposed in the literature with the same objective, however, in no case is the performance in sensitivity and specificity evaluated for the 3 preparation methods. The proposed algorithm improves the contrast of the colors of interest, then thresholds the image and segments by labeling the objects of interest (bacilli). Each object then has its geometrical descriptors and photometric descriptors. With all this, a characteristic vector is formed, which are used in the training and classification process of an SVM. For the training 225 images obtained by the 3 preparation methods were used. The proposed algorithm reached, for the direct method, a sensitivity level of 93.67% and a specificity level of 89.23%. In the case of the Pellet method, a sensitivity of 92.13% and a specificity of 82.58% was obtained, while for diluted Pellet the sensitivity was 92.81% and the specificity 83.61%.
Co-Authors Christian del Carpio Guillermo Kemper Jorge Coronel Mirko Zimic Patricia Sheen Roberto Lavarello