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IEICE Transactions on Information and Systems 2006 E89-D(3):1270-1279; doi:10.1093/ietisy/e89-d.3.1270
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Copyright © 2006 The Institute of Electronics, Information and Communication Engineers

Regular Section -- Papers -- Biological Engineering

A Simple Method for Detecting Tumor in T2-Weighted MRI Brain Images: An Image-Based Analysis

Phooi-Yee LAU and Shinji OZAWA

The authors are with the Department of Information and Computer Science, Keio Unviersity, Yokohama-shi, 223–8522 Japan. E-mail: laupy{at}ozawa.ics.keio.ac.jp

The objective of this paper is to present a decision support system which uses a computer-based procedure to detect tumor blocks or lesions in digitized medical images. The authors developed a simple method with a low computation effort to detect tumors on T2-weighted Magnetic Resonance Imaging (MRI) brain images, focusing on the connection between the spatial pixel value and tumor properties from four different perspectives: 1) cases having minuscule differences between two images using a fixed block-based method, 2) tumor shape and size using the edge and binary images, 3) tumor properties based on texture values using spatial pixel intensity distribution controlled by a global discriminate value, and 4) the occurrence of content-specific tumor pixel for threshold images. Measurements of the following medical datasets were performed: 1) different time interval images, and 2) different brain disease images on single and multiple slice images. Experimental results have revealed that our proposed technique incurred an overall error smaller than those in other proposed methods. In particular, the proposed method allowed decrements of false alarm and missed alarm errors, which demonstrate the effectiveness of our proposed technique. In this paper, we also present a prototype system, known as PCB, to evaluate the performance of the proposed methods by actual experiments, comparing the detection accuracy and system performance.

Key Words: medical image processing, tumor detection, brain tumor, block-based method, Sobel edge detector, global discriminate value, content-specific analysis

Manuscript received February 4, 2005. Manuscript revised July 25, 2005.


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