Automatic Segmentation and Classification of Mycobacterium Tuberculosis with Conventional Light Microscopy
Refereed conference paper presented and published in conference proceedings

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AbstractThis paper realizes the automatic segmentation and classification of Mycobacterium tuberculosis with conventional light microscopy. First, the candidate bacillus objects are segmented by the marker-based watershed transform. The markers are obtained by an adaptive threshold segmentation based on the adaptive scale Gaussian filter. The scale of the Gaussian filter is determined according to the color model of the bacillus objects. Then the candidate objects are extracted integrally after region merging and contaminations elimination. Second, the shape features of the bacillus objects are characterized by the Hu moments, compactness, eccentricity, and roughness, which are used to classify the single, touching and non-bacillus objects. We evaluated the logistic regression, random forest, and intersection kernel support vector machines classifiers in classifying the bacillus objects respectively. Experimental results demonstrate that the proposed method yields to high robustness and accuracy. The logistic regression classifier performs best with an accuracy of 91.68%.
All Author(s) ListXu C, Zhou DX, Zhai YP, Liu YH
Name of Conference9th International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR) - Parallel Processing of Images and Optimization; and Medical Imaging Processing
Start Date of Conference31/10/2015
End Date of Conference01/11/2015
Place of ConferenceEnshi
Country/Region of ConferenceChina
Journal nameProceedings of SPIE
Volume Number9814
LanguagesEnglish-United Kingdom
Keywordscomputer-aided diagnosis; Gaussian filter; Mycobacterium tuberculosis; watershed transform
Web of Science Subject CategoriesOptics

Last updated on 2021-25-09 at 00:33