Automatic segmentation and classification of mycobacterium tuberculosis with conventional light microscopy

Chao Xu; Dongxiang Zhou; Yongping Zhai; Yunhui Liu

doi:10.1117/12.2209245

14 December 2015 Automatic segmentation and classification of mycobacterium tuberculosis with conventional light microscopy

Chao Xu, Dongxiang Zhou, Yongping Zhai, Yunhui Liu

Author Affiliations +

Proceedings Volume 9814, MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing; 981409 (2015) https://doi.org/10.1117/12.2209245
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

This 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%.

Citation Download Citation

Chao Xu, Dongxiang Zhou, Yongping Zhai, and Yunhui Liu "Automatic segmentation and classification of mycobacterium tuberculosis with conventional light microscopy", Proc. SPIE 9814, MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing, 981409 (14 December 2015); https://doi.org/10.1117/12.2209245

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