Automated detection of tuberculosis in Ziehl‐Neelsen‐stained sputum smears using two one‐class classifiers

Khutlang, Rethabile; Krishnan, Sriram; Whitelaw, Andrew; Douglas, Tania S.

doi:10.1111/j.1365-2818.2009.03308.x

Cited by 45 publications

(17 citation statements)

References 11 publications

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“…[10] obtained sensitivity of 97.89% and specificity of 94.67% using a minimum error Bayesian classifier; they calculated accuracies per image (25× magnification) and not per object, thus a direct comparison with bacillus detection is not possible. Our results are also an improvement on those obtained previously using one-class pixel and object classification in ZN-stained smears [17]. …”

Section: Discussionsupporting

confidence: 73%

“…Pixel classifiers hold promise for the segmentation of bacilli from ZN-stained sputum smear images [17], because of their ability to exploit the color differences between bacilli and background in these images. The red color of the ZN carbol fuchsin stain is absorbed by the waxy coating of bacilli during staining, while the background is stained blue with a methylene blue counterstain.…”

Section: Introductionmentioning

confidence: 99%

“…[11] demonstrated color-based Bayesian segmentation of TB bacilli in images of ZN-stained sputum smears. We have previously used one-class classifiers for pixel and object classification in sputum smear images [17]. …”

Section: Introductionmentioning

confidence: 99%

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Classification of Mycobacterium tuberculosis in Images of ZN-Stained Sputum Smears

Khutlang

Krishnan

Dendere

et al. 2010

IEEE Trans. Inform. Technol. Biomed.

100

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Screening for tuberculosis (TB) in low-and middle-income countries is centered on the microscope. We present methods for the automated identification of Mycobacterium tuberculosis in images of Ziehl-Neelsen (ZN) stained sputum smears obtained using a bright-field microscope. We segment candidate bacillus objects using a combination of two-class pixel classifiers. The algorithm produces results that agree well with manual segmentations, as judged by the Hausdorff distance and the modified Williams index. The extraction of geometric-transformation-invariant features and optimization of the feature set by feature subset selection and Fisher transformation follow. Finally, different two-class object classifiers are compared. The sensitivity and specificity of all tested classifiers is above 95% for the identification of bacillus objects represented by Fisher-transformed features. Our results may be used to reduce technician involvement in screening for TB, and would be particularly useful in laboratories in countries with a high burden of TB, where, typically, ZN rather than auramine staining of sputum smears is the method of choice.

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Section: Discussionsupporting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classification of Mycobacterium tuberculosis in Images of ZN-Stained Sputum Smears

Khutlang

Krishnan

Dendere

et al. 2010

IEEE Trans. Inform. Technol. Biomed.

100

View full text Add to dashboard Cite

show abstract

“…An automated microscope for TB detection would include a motorised stage, an image capture unit and algorithms for auto-focusing [3], segmentation [4]–[7], classification [4], [5], [8] and auto-positioning.…”

Section: Introductionmentioning

confidence: 99%

Creating a virtual slide map from sputum smear images for region-of-interest localisation in automated microscopy

Patel

Douglas

2012

Computer Methods and Programs in Biomedicine

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We address the location of regions-of-interest in previously scanned sputum smear slides requiring reexamination in automated microscopy for tuberculosis (TB) detection. We focus on the core component of microscope auto-positioning, which is to find a point of reference, position and orientation, on the slide so that it can be used to automatically bring desired fields to the field-of-view of the microscope. We use virtual slide maps together with geometric hashing to localise a query image, which then acts as the point of reference. The true positive rate achieved by the algorithm was above 88% even for noisy query images captured at slide orientations up to 26°. The image registration error, computed as the average mean square error, was less than 14 pixel2 (corresponding to 1.02 μm2). The algorithm is inherently robust to changes in slide orientation and placement and showed high tolerance to illumination changes and robustness to noise.

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“…In Khutlang et al (2009Khutlang et al ( , 2010b, a two stage CBMIA system is designed to detect tuberculosis. In the first stage, a one-class classifier is used to extract transformation invariant shape features, including Fourier, moment and eccentricity compactness features; in the second Fig.…”

Section: Overview Of MM Classificationmentioning

confidence: 99%

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

Wang

2017

Artif Intell Rev

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Microorganisms such as protozoa and bacteria play very important roles in many practical domains, like agriculture, industry and medicine. To explore functions of different categories of microorganisms is a fundamental work in biological studies, which can assist biologists and related scientists to get to know more properties, habits and characteristics of these tiny but obbligato living beings. However, taxonomy of microorganisms (microorganism classification) is traditionally investigated through morphological, chemical or physical analysis, which is time and money consuming. In order to overcome this, since the 1970s innovative content-based microscopic image analysis (CBMIA) approaches are introduced to microbiological fields. CBMIA methods classify microorganisms into different categories using multiple artificial intelligence approaches, such as machine vision, pattern recognition and machine learning algorithms. Furthermore, because CBMIA approaches are semior full-automatic computer-based methods, they are very efficient and labour cost saving, supporting a technical feasibility for microorganism classification in our current big data age. In this article, we review the development history of microorganism classification using CBMIA approaches with two crossed pipelines. In the first pipeline, all related works are grouped by their corresponding microorganism application domains. By this pipeline, it is easy for microbiologists to have an insight into each special application domain and find their interested applied CBMIA techniques. In the second pipeline, the related works in each application domain are reviewed by time periods. Using this pipeline, computer scientists can see the dynamic of technological development clearly and keep up with the future devel-

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Automated detection of tuberculosis in Ziehl‐Neelsen‐stained sputum smears using two one‐class classifiers

Cited by 45 publications

References 11 publications

Classification of Mycobacterium tuberculosis in Images of ZN-Stained Sputum Smears

Classification of Mycobacterium tuberculosis in Images of ZN-Stained Sputum Smears

Creating a virtual slide map from sputum smear images for region-of-interest localisation in automated microscopy

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

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