Automated Color-Based Segmentation for Detection and Evaluation of Mycobacterium tuberculosis

• Main Authors: Chan-Yi Lin, 林展頤
• Other Authors: Yung-Nien Sun
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/32797912553369301421

碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 97 === This paper presents an automatic color-based Mycobacterium tuberculosis (MTB) detection method on optical microscopic images. The proposed method consists of two phases: detection of MTB candidates and classification. The detection phase is to find the location of MTB candidates and its processing steps include image preprocessing, detection of MTB candidates and calculation of feature parameters. In image preprocessing, we first designed a lightening compensation step to reduce the non-uniform brightness on microscopic images. Then, the microscopic images were classified into three types based on the variance of illumination. Next, a color normalization step is applied to reduce the color variation within the same type of images. The empty background which is light in color and occupies most area is then removed after normalization from the image. In the remaining textures, color features are extracted and evaluated by using Gaussian mixture model (GMM) to detect MTB candidates. By applying labeling and morphological methods, the candidates of MTB can be obtained and the corresponding parameters can be computed. In classification phase, different sets of parameters are selected to improve the classification accuracy for each type of images. The fuzzy logic classifier and the back-propagation neural network (BPN) were used for the classification of MTB. The experimental results show that the previous one has better performance than the later one. In summary, the proposed system can perform MTB detection automatically, efficiently and with good enough accuracy.