Automatic Segmentation and Classification of Mycobacterium Tuberculosis with Conventional Light Microscopy

作者：Xu Chao^*; Zhou Dongxiang; Zhai Yongping; Liu Yunhui

来源：9th International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR) - Parallel Processing of Images and Optimization; and Medical Imaging Processing, 2015-10-31 To 2015-11-01.

DOI：10.1117/12.2209245

• Mycobacterium tuberculosis
• watershed transform
• Gaussian filter
• computer-aided diagnosis

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%.

• 出版日期2015
• 单位中国人民解放军国防科学技术大学; 香港中文大学