Separation of overlapping bacilli in microscopic digital TB images

Priya, E.; Srinivasan, S.

doi:10.1016/j.bbe.2014.08.002

Cited by 25 publications

(13 citation statements)

References 13 publications

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“…To separate the overlapping bacilli in the sputum smear images, Priya Ebenezer and Srinivasan Subramanian [16] developed the method of concavity (MOC). The performance of MOC was compared with that of Multi-phase Active Contour (MAC) and Marker-Controlled Watershed (MCW) based on the evaluation using statistical mean quality score.…”

Section: Motivationmentioning

confidence: 99%

“…• One of the major challenges in processing the sputum smear images that performs the separation of overlapping bacilli is the higher density of bacilli. Estimating the size and the shape of the image without the separation of touching and overlapping TB objects may result in gross errors during the identification and classification of images [16]. • The human eye has substantial sharpness to discriminate the fine details that consist of high-frequency information, whereas it is not sensitive to images of low frequency or that is which is slowly varying.…”

Section: Challengesmentioning

confidence: 99%

“…Moreover, the techniques in ref. [4,8,10,11,16,19] were based on traditional classifiers such as SVM, neural network and RF classifier, which are not suitable for the overlapping bacilli.…”

Section: Challengesmentioning

confidence: 99%

“…Therefore, it is necessary to develop an automatic diagnostic process to enhance the sensitivity and the accuracy of the test. Pattern-recognition [14,15] and image-processing methods are the promising tools employed for automatic screening of TB sputum smear images [16].…”

Section: Introductionmentioning

confidence: 99%

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

Mithra

Emmanuel

2018

Sādhanā

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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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Section: Motivationmentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

“…Moreover, the techniques in ref. [4,8,10,11,16,19] were based on traditional classifiers such as SVM, neural network and RF classifier, which are not suitable for the overlapping bacilli.…”

Section: Challengesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Mithra

Emmanuel

2018

Sādhanā

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“…However, the identification and location of overlapping apples is still a problem which restricts the development of harvesting robot. To solve this problem, numerous scholars have carried out a lot of researches on the identification and location of overlapping images and some progress has been made [10][11][12][13][14][15][16][17]. Among them, Petros [10] used watershed transform and gradient paths to segment touching and overlapping chromosomes.…”

Section: Introductionmentioning

confidence: 99%

Recognition and Localization Method of Overlapping Apples for Apple Harvesting Robot

Shen

Zhao

Jia

et al. 2016

Computer and Computing Technologies in Agriculture IX

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In order to meet the speed requirements of harvesting robot, a method of tracking and recognition of overlapping apples is proposed in this paper. First of all, the first image should be segmented and denoised, the center and the radius is determined, the template which is used for matching is extracted according to the center and radius. Then, determine the center of ten images which are taken continuously, fit motion path of the robot according to the center of each image and predict subsequent motion path. The next processing area is determined according to the radius and predicted path. Finally, overlapping apples are identified by fast normalized cross correlation match method. Experiments prove that the new method can locate the center and radius of overlapping apples correctly. Besides, matching time is reduced by 48.1% compared with the original one.

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A Brief Review for Content-Based Microorganism Image Analysis Using Classical and Deep Neural Networks

Jiang

et al. 2018

Advances in Intelligent Systems and Computing

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Separation of overlapping bacilli in microscopic digital TB images

Cited by 25 publications

References 13 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

Recognition and Localization Method of Overlapping Apples for Apple Harvesting Robot

A Brief Review for Content-Based Microorganism Image Analysis Using Classical and Deep Neural Networks

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