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Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 619658, 10 pages
Research Article

Classification of Cerebral Lymphomas and Glioblastomas Featuring Luminance Distribution Analysis

1School of Electrical and Computer Engineering, Cornell University, Phillips Hall, Ithaca, NY 14853-5401, USA
2Department of Information and Communications Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
3Department of Diagnostic Radiology, Kumamoto University, 1-1-1 Honjo, Kumamoto City, Kumamoto 860-8556, Japan
4Department of Medical Imaging, Kumamoto University, 4-24-1 Kuhonji, Kumamoto City, Kumamoto 862-0976, Japan

Received 17 January 2013; Accepted 20 May 2013

Academic Editor: Yi-Hong Chou

Copyright © 2013 Toshihiko Yamasaki 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.


Differentiating lymphomas and glioblastomas is important for proper treatment planning. A number of works have been proposed but there are still some problems. For example, many works depend on thresholding a single feature value, which is susceptible to noise. In other cases, experienced observers are required to extract the feature values or to provide some interactions with the system. Even if experts are involved, interobserver variance becomes another problem. In addition, most of the works use only one or a few slice(s) because 3D tumor segmentation is time consuming. In this paper, we propose a tumor classification system that analyzes the luminance distribution of the whole tumor region. Typical cases are classified by the luminance range thresholding and the apparent diffusion coefficients (ADC) thresholding. Nontypical cases are classified by a support vector machine (SVM). Most of the processing elements are semiautomatic. Therefore, even novice users can use the system easily and get the same results as experts. The experiments were conducted using 40 MRI datasets. The classification accuracy of the proposed method was 91.1% without the ADC thresholding and 95.4% with the ADC thresholding. On the other hand, the baseline method, the conventional ADC thresholding, yielded only 67.5% accuracy.