Lymph Node Image Segmentation Based on Improved FCM Clustering and Multi-Threshold

Abstract:

The pathological change of lymph node is an important basis of malignant tumor detection and judgment of metastasis of cancer (lung cancer, colorectal cancer, breast cancer, liver cancer, cervical cancer, etc.) An algorithm of lymph node image segmentation based on improved FCM clustering and multi-threshold is proposed to segment the lymph CT image with blurred edge. First, the improved FCM peak clustering is used to sharpen the fuzzy boundary of lymph CT image effectively. Then the multi-threshold algorithm based on image entropy change is introduced to segment enhanced images. The experiment shows that the above algorithm can obtain better segmentation results compared with the traditional FCM clustering method in the case of the fuzzy edge of the lymph node tissue.

• Periodical: Advanced Materials Research (Volumes 760-762)
• Pages: 1510-1514
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.760-762.1510
• Online since: September 2013
• Authors: Yan Ling Zhang, Yue Jia Zhang, Li Li
• Keywords: Fuzzy C-Means (FCM) Peak Algorithm, Lymph Node Image Segmentation, Multi-Threshold Algorithm

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