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

Ching-Hsue Cheng; Liang-Ying Wei

doi:10.6180/jase.2016.19.1.08

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

Computer Science and Information Engineering

Ching-Hsue Cheng¹ and Liang-Ying Wei This email address is being protected from spambots. You need JavaScript enabled to view it.²

¹Department of Information Management, National Yunlin University of Science and Technology, Yunlin, Taiwan 640, R.O.C.
²Department of Information Management, Yuanpei University of Medical Technology, Hsin Chu, Taiwan 300, R.O.C.

Received: August 25, 2015
Accepted: October 31, 2015
Publication Date: March 1, 2016

Download Citation: ||https://doi.org/10.6180/jase.2016.19.1.08

ABSTRACT

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.

Keywords: Computer-aided Detection, Liver Tumor, Abdominal CT Image, Wavelet Packet Transform, Rough Set Theory

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