Table of Contents Author Guidelines Submit a Manuscript
International Journal of Biomedical Imaging
Volume 2014, Article ID 947539, 12 pages
http://dx.doi.org/10.1155/2014/947539
Research Article

Ischemic Stroke Detection System with a Computer-Aided Diagnostic Ability Using an Unsupervised Feature Perception Enhancement Method

1School of Medicine, Chung Shan Medical University, No. 110, Section 1, Jianguo North Road, Taichung 40201, Taiwan
2Department of Medical Imaging, Chung Shan Medical University Hospital, No. 110, Section 1, Jianguo North Road, Taichung 40201, Taiwan
3School of Medical Imaging and Radiological Sciences, Chung Shan Medical University, No. 110, Section 1, Jianguo North Road, Taichung 40201, Taiwan
4School of Medical Informatics, Chung Shan Medical University, No. 110, Section 1, Jianguo North Road, Taichung 40201, Taiwan

Received 21 July 2014; Revised 2 November 2014; Accepted 11 November 2014; Published 9 December 2014

Academic Editor: Richard H. Bayford

Copyright © 2014 Yeu-Sheng Tyan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We propose an ischemic stroke detection system with a computer-aided diagnostic ability using a four-step unsupervised feature perception enhancement method. In the first step, known as preprocessing, we use a cubic curve contrast enhancement method to enhance image contrast. In the second step, we use a series of methods to extract the brain tissue image area identified during preprocessing. To detect abnormal regions in the brain images, we propose using an unsupervised region growing algorithm to segment the brain tissue area. The brain is centered on a horizontal line and the white matter of the brain’s inner ring is split into eight regions. In the third step, we use a coinciding regional location method to find the hybrid area of locations where a stroke may have occurred in each cerebral hemisphere. Finally, we make corrections and mark the stroke area with red color. In the experiment, we tested the system on 90 computed tomography (CT) images from 26 patients, and, with the assistance of two radiologists, we proved that our proposed system has computer-aided diagnostic capabilities. Our results show an increased stroke diagnosis sensitivity of 83% in comparison to 31% when radiologists use conventional diagnostic images.