期刊名称：International Journal of Computer Science, Engineering and Applications (IJCSEA)
印刷版ISSN：2231-0088
电子版ISSN：2230-9616
出版年度：2012
卷号：2
期号：3
DOI：10.5121/ijcsea.2012.2314
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Early detection of lung cancer is a challenging problem, the world faces today. Prior to classify glandular cells as malignant or benign a reliable segmentation technique is required. In this paper we present a novel lung glandular cell segmentation technique. The technique uses a combination of multiple color spaces and various clustering algorithms to automatically find the best possible segmentation result. Unsupervised clustering methods of K-means and Fuzzy C-means were used on multiple color spaces such as HSV, LAB, LUV, xyY. Experimental results of segmentation using various color spaces are provided to show the performance of the proposed system.
关键词：K-means; Fuzzy C-means; Color space; Sputum cytology
