Title :
Stomach Epidermis Tumor Cell Segmentation Based on the Maximization of Mutual Information in Effective Information
Author :
Gan, Nan ; Zheng, Fei-hu
Author_Institution :
Comput. Coll., Nanjing Univ. of Sci. & Technol., Nanjing, China
Abstract :
In the complex stomach epidermis tumor cells, the traditional segmentation algorithms such as the K-means clustering algorithm and the simple threshold segmentation algorithm are unable to get satisfactory results. The relaxation iterative segmentation algorithm can segment the cell clearly, but it wastes a lot of time and the execution efficiency is very low. In this paper the authors propose a new segmentation algorithm based on the maximization of Mutual information in effective information, in which to find the optimal threshold values to segment the stomach epidermis tumor cells.
Keywords :
image segmentation; iterative methods; medical image processing; optimisation; tumours; K-mean clustering algorithm; image thresholding; iterative segmentation algorithm; stomach epidermis tumor cell segmentation; Clustering algorithms; Educational institutions; Epidermis; Iterative algorithms; Mutual information; Stomach; Tumors;
Conference_Titel :
Pattern Recognition, 2009. CCPR 2009. Chinese Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4199-0
DOI :
10.1109/CCPR.2009.5344086