Abstract
This paper proposes an automated detection of tuberculosis bacilli in Ziehl-Neelsen-stained tissue slides using image processing and neural network. Image segmentation using CY-based colour filter and k-mean clustering procedure is used to separate objects of interest from the background. A number of geometrical features are then extracted from the segmented images. A recent training algorithm called Extreme Learning Machine (ELM) is modified to train a hybrid multilayered perceptron network (HMLP) for the classification task. The results indicate that the performance of HMLP-ELM network is comparable to the previously proposed methods and offers a fast training time with no designing parameter required. Ill. 6, bibl. 15, tabl. 1 (in English; abstracts in English and Lithuanian).DOI: http://dx.doi.org/10.5755/j01.eee.120.4.1456
Highlights
Tuberculosis, commonly known as TB, is a killer disease caused by infection by Mycobacterium tuberculosis
The clinical diagnosis of TB is performed by a microscopic examination using either the fluorescence microscope or light microscope
In order to proof the robustness of the proposed method, 125 tissue slide images were selected so that they consist of various staining conditions such as properly stained, understained and overstained images
Summary
Tuberculosis, commonly known as TB, is a killer disease caused by infection by Mycobacterium tuberculosis. The bacteria usually attacks the lung causing pulmonary TB (PTB), yet there are cases where it strikes other parts of the human body, referred as extrapulmonary TB (EPTB). The clinical diagnosis of TB is performed by a microscopic examination using either the fluorescence microscope or light microscope. For PTB, the diagnosis is conducted by the sputum examination. For EPTB, the biopsied tissue of the infected organ is used for diagnosis. Clinical specimens are stained using auraminerhodamine stain for analysis using fluorescence microscope while Ziehl-Neelsen (ZN) stain is used for the light microscope
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