Rough Classifier Based on Region Growth Algorithm for Identifying Liver CT Image

doi:10.6180/jase.2016.19.1.08

淡江大學機構典藏 > 淡江出版期刊 > 淡江理工學刊 > 第19卷第1期 > Item 987654321/108941

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题名:	Rough Classifier Based on Region Growth Algorithm for Identifying Liver CT Image
作者:	Cheng, Ching-Hsue;Wei, Liang-Ying
关键词:	Computer-aided Detection;Liver Tumor;Abdominal CT Image;Wavelet Packet Transform;Rough Set Theory
日期:	2016-03
上传时间:	2016-12-20 09:40:45 (UTC+8)
出版者:	淡江大學出版中心
摘要:	Over decades, liver cancer is a rising cause of death in Taiwan, and more and more researchers are concerned about detecting hepatic tumors in computed tomography (CT) images. For clinical applications in terms of diagnosis and treatment planning, image segmentation on abdominal CT is indispensable. Patients with a large number of CT images need specialist physicians to identify, and detecting tumor location correctly from many CT images has been a major challenge subsequently. Therefore, this paper proposed a novel computer-aided detection (CAD) method that had high classification accuracy for identifying tumors. The proposed method used a region growing algorithm to segment liver CT images, employed REDUCT sets to reduce attributes, and then utilized a rough set algorithm to enhance classification performance. To evaluate the classification performances, the proposed method was compared with five different classification methods: decision tree (C4.5 and REP (reduced error pruning)), multilayer perceptron, Naïve Bayes, and support vector machine (SVM). The results indicate that the proposed method is superior to the listing methods in terms of classification accuracy.
關聯:	Journal of Applied Science and Engineering 19(1), pp.65-74
DOI:	10.6180/jase.2016.19.1.08
显示于类别:	[淡江理工學刊] 第19卷第1期

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