A color-based approach for automated segmentation in tumor tissue classification.
This paper presents a new color-based approach for automated segmentation and classification of tumor tissues from microscopic images. The method comprises three stages: (1) color normalization to reduce the quality variation of tissue image within samples from individual subjects or from different subjects; (2) automatic sampling from tissue image to eliminate tedious and time-consuming steps; and (3) principal component analysis (PCA) to characterize color features in accordance with a standard set of training data. We evaluate the algorithm by comparing the performance of the proposed fully-automated method against semi-automated procedures. Experimental studies show consist agreement between the two methods. Thus, the proposed algorithm provides an effective tool for evaluating oral cancer images. It can also be applied to other microscopic images prepared with the same type of tissue staining.
Wang
Yi-Ying
YY
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan.
Chang
Shao-Chien
SC
Wu
Li-Wha
LW
Tsai
Sen-Tien
ST
Sun
Yung-Nien
YN
D000465:Algorithms; D003116:Color; D006801:Humans; D007091:Image Processing, Computer-Assisted; D009062:Mouth Neoplasms / Q000145:classification*