Universiti Teknologi Malaysia Institutional Repository

Adaptive crossed reconstructed (ACR) K-mean clustering segmentation for computer-aided bone age assessment system

Hum, Yan Chai and Lai, Khin Wee and Tan, Tian Swee and Shaikh Salleh, Sheikh Hussain (2011) Adaptive crossed reconstructed (ACR) K-mean clustering segmentation for computer-aided bone age assessment system. International Journal of Mathematical Models and Methods in Applied Sciences, 5 (3). pp. 628-635. ISSN 1998-0140

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Official URL: http://www.naun.org/main/NAUN/ijmmas/2011.html

Abstract

The development of computer-aided design (CAD) system for clinical usage has been given excessive attention in recent years. Nonetheless, many problems still remain unsolved in the CAD field especially the segmentation problem in digital image processing. In order to increase the accuracy and efficiency in Bone age assessment (BAA), CAD system has been developed to assist the doctor and radiologist. The crucial step in the system is the bone segmentation before proceeding to the subsequent analysis and comparison with atlas. Therefore, in this paper, a method proposed to solve the problem based on grey-level co-occurrence matrix (GLCM) and k-means clustering, namely adaptive crossing reconstruction (ACR) k-mean clustering method. The method begins with bands separations into vertical and horizontal direction. Next, the pixels of each section are clustered and performed with GLCM texture analysis. At last, all the sections will be reconstructed based on the texture analysis. The resulting outcome shows that this method could segment the bone from the soft-tissue region and background effectively compared to global clustering method.

Item Type:Article
Uncontrolled Keywords:bone age assessment, gray level co-occurrence matrix, image processing, skeletal segmentation, textural segmentation
Subjects:Q Science
Divisions:Biosciences and Medical Engineering
ID Code:28655
Deposited By: Yanti Mohd Shah
Deposited On:12 Nov 2012 15:26
Last Modified:28 Jan 2019 11:35

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