Detection and Quantification of Bacilli and Clusters Present in Sputum Smear Samples: A Novel Algorithm for Pulmonary Tuberculosis Diagnosis

Sotaquirá, Miguel; Rueda, Luis Alberto Vazquez; Narvaez, R.

doi:10.1109/icdip.2009.59

Cited by 40 publications

(29 citation statements)

References 5 publications

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“…In addition to the huge variability in sensitivity, the manual screening for bacillus identification is a labor-intensive task that consumes between 40 minutes and 3 hours, depending on patient's level of infection and it is needed to analyse 40-100 images (Sotaquirá, 2009). …”

Section: Introductionmentioning

confidence: 99%

Sputum Smear Microscopy for Tuberculosis: Evaluation of Autofocus Functions and Automatic Identification of Tuberculosis Mycobacterium

Filho¹,

Costa²

2012

Understanding Tuberculosis - Global Experiences and Innovative Approaches to the Diagnosis

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Section: Introductionmentioning

confidence: 99%

Sputum Smear Microscopy for Tuberculosis: Evaluation of Autofocus Functions and Automatic Identification of Tuberculosis Mycobacterium

Filho¹,

Costa²

2012

Understanding Tuberculosis - Global Experiences and Innovative Approaches to the Diagnosis

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“…As shown in Table 1, only one report (Sotaquirá et al, 2009) cited time wasted for image analysis. It is necessary to consider the number of images required to achieve a correct diagnosis to compute the time consumed with a TB automatic diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…The first methods for automatic bacillus screening in conventional microscopy were published in 2008 (Costa et al, 2008;Sadaphal et al, 2008;Raof et al, 2008). Recently, other methods for automatic bacillus screening were published (Forero et al, 2004(Forero et al, , 2006Khutlang et al, 2010;Lenseigne et al, 2007;Makkapati, et al, 2009;Osman et al, 2012;Sotaquirá et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Some authors (Forero et al, 2006;Khutlang et al, 2010;Sotaquirá et al, 2009) claim that the advantages of automatic bacillus screening over a manual screening include more reproducible values for sensitivity and specificity and a faster screening process. Table 1 reports the values for sensitivity, specificity and time waste for one image analysis using automatic methods.…”

Section: Introductionmentioning

confidence: 99%

“…Khutlang et al (2010) uses the RGB color space but did not justify their choice. Sotaquirá et al (2009) analyzed the following color spaces: RGB, YCbCr, Lab, YIQ and HSV. From this analysis, the authors conclude that RGB, HSV and YIQ are not adequate because they generate a high number of false positives after the segmentation stage.…”

Section: Introductionmentioning

confidence: 99%

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Automatic identification of tuberculosis mycobacterium

et al. 2015

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Introduction: According to the Global TB control report of 2013, "Tuberculosis (TB) remains a major global health problem. In 2012, an estimated 8.6 million people developed TB and 1.3 million died from the disease. Two main sputum smear microscopy techniques are used for TB diagnosis: Fluorescence microscopy and conventional microscopy. Fluorescence microscopy is a more expensive diagnostic method because of the high costs of the microscopy unit and its maintenance. Therefore, conventional microscopy is more appropriate for use in developing countries. Methods: This paper presents a new method for detecting tuberculosis bacillus in conventional sputum smear microscopy. The method consists of two main steps, bacillus segmentation and post-processing. In the fi rst step, the scalar selection technique was used to select input variables for the segmentation classifi ers from four color spaces. Thirty features were used, including the subtractions of the color components of different color spaces. In the post-processing step, three fi lters were used to separate bacilli from artifact: a size fi lter, a geometric fi lter and a Rule-based fi lter that uses the components of the RGB color space. Results: In bacillus identifi cation, an overall sensitivity of 96.80% and an error rate of 3.38% were obtained. An image database with 120-sputum-smear microscopy slices of 12 patients with objects marked as bacillus, agglomerated bacillus and artifact was generated and is now available online. Conclusions: The best results were obtained with a support vector machine in bacillus segmentation associated with the application of the three post-processing fi lters.

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Identification of robust focus measure functions for the automated capturing of focused images from Ziehl–Neelsen stained sputum smear microscopy slide

et al. 2017

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Capturing of the best-focused image using focus measure function (FMF) from a stack of images acquired at different focus distances is a crucial step in automatic microscopy development. Detection of bacilli present in Ziehl-Neelsen (ZN) stained sputum smears conventional microscope (CM) images is critical to disease diagnosis. Studies have revealed that the performances of FMFs are sensitive to image contents and background noise. In this article, 24 diverse FMFs were implemented on 31 stacks of CM's field of view images acquired from three different microscopes to determine the best-focused one. Seven FMFs achieved the accuracies of greater than 90%. Accuracy, focus error, and false maxima were calculated for each FMF, and overall score and ranking were also calculated for better interpretation. Preprocessing techniques such as filtering and image distortions (noise, contrast, saturation, illumination, etc.) were performed to evaluate the robustness of every FMF. Gaussian derivative, steerable filters, Tenengrad, and Hemli and Scherer's mean FMFs were identified as the most robust and accurate functions with the accuracy >90%. These FMFs have a relatively less focus error and false maxima rate. Full widths at half maximum of these four FMFs were also computed to determine their efficacy for the optimization process. These four FMFs can be implemented for automated capturing of the image from ZN-stained sputum smear slide. Gaussian derivative FMF can also be used effectively for both CM and fluorescence microscope's field of view image stacks to determine the best-focused one from each stack. © 2017 International Society for Advancement of Cytometry.

show abstract

Detection and Quantification of Bacilli and Clusters Present in Sputum Smear Samples: A Novel Algorithm for Pulmonary Tuberculosis Diagnosis

Cited by 40 publications

References 5 publications

Sputum Smear Microscopy for Tuberculosis: Evaluation of Autofocus Functions and Automatic Identification of Tuberculosis Mycobacterium

Sputum Smear Microscopy for Tuberculosis: Evaluation of Autofocus Functions and Automatic Identification of Tuberculosis Mycobacterium

Automatic identification of tuberculosis mycobacterium

Identification of robust focus measure functions for the automated capturing of focused images from Ziehl–Neelsen stained sputum smear microscopy slide

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