A new algorithm for automatic assessment of the degree of TB-infection using images of ZN-stained sputum smear

Tuberculosis (TB) is one of the infectious diseases spread by the infectious agent Mycobacterium tuberculosis. Sputum smear microscopy is the primary tool used for the diagnosis of pulmonary TB, but has its limitations such as low sensitivity and large observation time. Hence, an automated technique is preferred for the diagnosis of TB. This paper develops a technique for TB diagnosis based on the bacilli count by proposing Fuzzy and Hyco-entropy-based Decision Tree (FHDT) classifier using sputum smear microscopic images. The proposed technique involves three steps: segmentation, feature extraction and classification. Initially, the input sputum smear microscopic image is subjected to a colour space transformation, for which a thresholding is applied to obtain the segmented result. Important features such as length, density, area and few histogram features are extracted for FHDT-based classification that classifies the segments into few-bacilli, non-bacilli and overlapping bacilli. An entropy function, called hyco-entropy, is designed for the optimal selection of feature. For further analysis of classification, that is, to count the number in the overlapping bacilli, the fuzzy classifier is adopted. FHDT classifier is evaluated in terms of Segmentation Accuracy (SA), Mean Squared Error (MSE) and Missing Count (MC) using microscopic images taken from ZNSM-iDB, where it can attain maximum mean SA of 0.954 and mean MC of 2.4.

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“…where HC f j À Á is the hyco-entropy and CHC f j ; f q À Á is the conditional hyco-entropy, which are defined in Eqs. (18) and (19).…”

Section: 3bmentioning

confidence: 99%

“…An automatic system of TB detection can inspect the existence of TB bacteria easily and automatically from the focused images with or without human intervention. The steps involved in the automation [18] are pre-processing, segmentation, feature extraction and classification.…”

Section: Introductionmentioning

confidence: 99%

FHDT: Fuzzy and Hyco-entropy-based Decision Tree Classifier for Tuberculosis Diagnosis from Sputum Images

Mithra

Emmanuel

2018

Sādhanā

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“…Thus, a complete automatic TB detection system holds the capability for analyzing the existence of TB bacteria automatically with the increased speed without involving the humans. The automatic system determines the existence of bacilli using the following steps: pre‐processing, segmentation, extraction of features, and classification 16 …”

Section: Introductionmentioning

confidence: 99%

Severity detection and infection level identification of tuberculosis using deep learning

Chithra

Jagatheeswari

2020

Int J Imaging Syst Tech

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Tuberculosis (TB) is a highly infectious disease and is one of the major health problems all over the world. The accurate detection of TB is a major challenge faced by most of the existing methods. This work addresses these issues and developed an effective mechanism for detecting TB using deep learning. Here, the color space transformation is applied for transforming the red green and blue image to LUV space, where L stands for luminance, U and V represent chromaticity values of color images. Then, adaptive thresholding is carried out for image segmentation and various features, like coverage, density, color histogram, area, length, and texture features, are extracted to enable effective classification. After the feature extraction, the size of the features is reduced using principal component analysis. The extracted features are subjected to fractional crow search‐based deep convolutional neural network (FC‐SVNN) for the classification. Then, the image level features, like bacilli count, bacilli area, scattering coefficients and skeleton features are considered to perform severity detection using proposed adaptive fractional crow (AFC)‐deep CNN. Finally, the inflection level is determined using entropy, density and detection percentage. The proposed AFC‐Deep CNN algorithm is designed by modifying FC algorithm using self‐adaptive concept. The proposed AFC‐Deep CNN shows better performance with maximum accuracy value as 0.935.

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“…Makkapati et al, 2009, presented a method for bacilli recognition segmenting the image using Otsu's technique and searching for a beaded structure inside the segmented objects in order to classify them. Nayak et al, 2010, proposed the segmentation based on minimum distance between clusters of bacilli pixels and non-bacilli pixels in the colour space HSV. Zhai et al, 2010, proposed the segmentation based on the colour spaces HSI and Lab followed by a decision tree to classify the segmented objects using area, roughness and circularity.…”

Section: Introductionmentioning

confidence: 99%

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

Soares

Côco

Salles

et al. 2015

Proceedings of the 10th International Conference on Computer Vision Theory and Applications

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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.

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A new algorithm for automatic assessment of the degree of TB-infection using images of ZN-stained sputum smear

Cited by 32 publications

References 5 publications

FHDT: Fuzzy and Hyco-entropy-based Decision Tree Classifier for Tuberculosis Diagnosis from Sputum Images

FHDT: Fuzzy and Hyco-entropy-based Decision Tree Classifier for Tuberculosis Diagnosis from Sputum Images

Severity detection and infection level identification of tuberculosis using deep learning

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

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