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Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 145926, 9 pages
Research Article

A Novel Approach to Segment and Classify Regional Lymph Nodes on Computed Tomography Images

1School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
2State Key Laboratory of Oncology in Southern China, Imaging Diagnosis and Interventional Center, Cancer Center, Sun Yat-Sen University, Guangzhou 510060, China
3Department of Automation, Sun Yat-Sen University, Guangzhou 510006, China

Received 19 June 2012; Revised 21 September 2012; Accepted 28 September 2012

Academic Editor: Henggui Zhang

Copyright © 2012 Hongmin Cai 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.


Morphology of lymph nodal metastasis is critical for diagnosis and prognosis of cancer patients. However, accurate prediction of lymph node type based on morphological information is rarely available due to lack of pathological validation. To obtain correct morphological information, lymph nodes must be segmented from computed tomography (CT) image accurately. In this paper we described a novel approach to segment and predict the status of lymph nodes from CT images and confirmed the diagnostic performance by clinical pathological results. We firstly removed noise and preserved edge details using a revised nonlinear diffusion equation, and secondly we used a repulsive-force-based snake method to segment the lymph nodes. Morphological measurements for the characterization of the node status were obtained from the segmented node image. These measurements were further selected to derive a highly representative set of node status, called feature vector. Finally, classical classification scheme based on support vector machine model was employed to simulate the prediction of nodal status. Experiments on real clinical rectal cancer data showed that the prediction performance with the proposed framework is highly consistent with pathological results. Therefore, this novel algorithm is promising for status prediction of lymph nodes.