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Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 947191, 8 pages
http://dx.doi.org/10.1155/2012/947191
Research Article

Two-Dimensional Matrix Algorithm Using Detrended Fluctuation Analysis to Distinguish Burkitt and Diffuse Large B-Cell Lymphoma

1Department of Mechanical Engineering, Yuan Ze University, 135 Yuan-Tung Road, Chungli 32003, Taiwan
2Department of Pathology, National Taiwan University Hospital, Taipei 100, Taiwan
3School of Engineering and Design, Brunel University, London UB8 3PH, UK
4Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chungli 32001, Taiwan

Received 19 September 2012; Accepted 19 November 2012

Academic Editor: Wenxiang Cong

Copyright © 2012 Rong-Guan Yeh 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

A detrended fluctuation analysis (DFA) method is applied to image analysis. The 2-dimensional (2D) DFA algorithms is proposed for recharacterizing images of lymph sections. Due to Burkitt lymphoma (BL) and diffuse large B-cell lymphoma (DLBCL), there is a significant different 5-year survival rates after multiagent chemotherapy. Therefore, distinguishing the difference between BL and DLBCL is very important. In this study, eighteen BL images were classified as group A, which have one to five cytogenetic changes. Ten BL images were classified as group B, which have more than five cytogenetic changes. Both groups A and B BLs are aggressive lymphomas, which grow very fast and require more intensive chemotherapy. Finally, ten DLBCL images were classified as group C. The short-term correlation exponent α1 values of DFA of groups A, B, and C were , and , respectively. It was found that α1 value of BL image was significantly lower ( ) than DLBCL. However, there is no difference between the groups A and B BLs. Hence, it can be concluded that α1 value based on DFA statistics concept can clearly distinguish BL and DLBCL image.