Support Vector Machines for Automatic Detection of Tuberculosis Bacteria in Confocal Microscopy Images

Lenseigne, Boris; Brodin, Priscille; Jeon, Hee Kyoung; Christophe, Thierry; Genovesio, Auguste

doi:10.1109/isbi.2007.356794

Cited by 18 publications

(9 citation statements)

References 4 publications

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“…Bacteria were detected using a classification method based on a support vector machine algorithm, allowing precise segmentation of the pixels positive for a GFP signal versus background signals. 17 A cell was considered infected if at least two contiguous pixels of bacteria were found within their cytoplasm. The ratio of infected cells was calculated by dividing the number of infected cells by the total number of cells.…”

Section: Cytotoxicity and Intracellular Assaysmentioning

confidence: 99%

High-Content Screening of Raw Actinomycete Extracts for the Identification of Antituberculosis Activities

et al. 2017

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The feasibility and relevance of screening a library of raw actinomycete extracts (ECUM library) for the identification of antituberculosis activities was assessed on 11,088 extracts using a multiple-screening approach. Each extract was first tested at two concentrations against noninfected macrophages as a control, then against Mycobacterium tuberculosis growing in broth medium as well as infecting murine macrophages. The screening results indicated a library of good quality with an apparent low proportion of cytotoxic extracts. A correlation was found between both bacterial assays, but the intracellular assay showed limitations due to low rates of cell survival. Several extracts of interest were highlighted by this multiple screening. A focus on the strain producing the two most effective revealed similarities with known producers of active molecules, suggesting the possibility of selecting relevant extracts using this strategy.

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Section: Cytotoxicity and Intracellular Assaysmentioning

confidence: 99%

High-Content Screening of Raw Actinomycete Extracts for the Identification of Antituberculosis Activities

et al. 2017

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“…Others have applied machine learning techniques to this task for less complex types of (typically cultured) cells, meaning the background is relatively clean and cell shapes are simple in comparison with our data. Examples include neural networks applied to detect white blood cells [15] or fluorescent marked lymphocytes [11], and Support Vector Machines used to detect lymphoma cells [9] and tuberculosis bacteria [7].…”

Section: Related Workmentioning

confidence: 99%

Fast Ray features for learning irregular shapes

Smith

Carleton

Lepetit

2009

2009 IEEE 12th International Conference on Computer Vision

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We introduce a new class of image features, the Ray feature set, that consider image characteristics at distant contour points, capturing information which is difficult to represent with standard feature sets. This property allows Ray features to efficiently and robustly recognize deformable or irregular shapes, such as cells in microscopic imagery. Experiments show Ray features clearly outperform other powerful features including Haar-like features and Histograms of Oriented Gradients when applied to detecting irregularly shaped neuron nuclei and mitochondria. Ray features can also provide important complementary information to Haar features for other tasks such as face detection, reducing the number of weak learners and computational cost. Ray features can be efficiently precomputed to reduce cost, just as precomputing integral images reduces the overall cost of Haar features. While Rays are slightly more expensive to precompute, their computational cost is less than that of Haar features for scanning an AdaBoost-based detector window across an image at run-time

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“…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%

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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Support Vector Machines for Automatic Detection of Tuberculosis Bacteria in Confocal Microscopy Images

Cited by 18 publications

References 4 publications

High-Content Screening of Raw Actinomycete Extracts for the Identification of Antituberculosis Activities

High-Content Screening of Raw Actinomycete Extracts for the Identification of Antituberculosis Activities

Fast Ray features for learning irregular shapes

Automatic identification of tuberculosis mycobacterium

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