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CT Brain Image:Abnormalities Recognition and Segmentation 被引量:1
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作者 TONG Hau-Lee Mohammad Faizal Ahmad Fauzi +1 位作者 ryoichi komiya HAW Su-Cheng 《Journal of Donghua University(English Edition)》 EI CAS 2010年第2期246-249,共4页
In order to develop an automated segmentation system for Computed Tomography (CT) brain images, a new approach which consists of several unsupervised segmcotation techniques was introduced. The system segments the C... In order to develop an automated segmentation system for Computed Tomography (CT) brain images, a new approach which consists of several unsupervised segmcotation techniques was introduced. The system segments the CT brain images into three partitions, i. e., abnormalities, cerebrospinal fluid (CSF), and brain matter. Our approach consists of two phase-segmentation methods. In the first phase segmentation, k-means and fuzzy cmeans (FCM) methods were implemented to segment and transform the images into the binary images. Based on the connected component in binary images, a decision tree was employed for the annotation of normal or abnormal regions. In the second phase segmentation, the modified FCM with population-diameter independent (PDI) segmentation was applied to segment the images into CSF and brain matter. The experimental results have shown that our proposed system is feasible and yield satisfactory results. 展开更多
关键词 computed tomography ursupervised segmentation K-MEANS fuzzy c-means population-diameter indepentdent
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