Image Feature Analysis of Lymph Node Based on Cloud-Model and FCM Clustering

Lymph node is one of the important organs as the immunologic filter in the body. 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.). Therefore, the feature analysis of lymph node is meaningful for the forecast about tumor recrudescence and metastasis. Based on image features, lymph node characters and advice from doctors, the sixteen features including shape, texture and grey are extracted and analyzed using cloud model. Eight helpful features (the largest area ratio, length to diameter ratio, the area ratio of length to diameter, the standard deviation of the internal texture changes, standard differential gray, gray consistency, low density values, high density value) for classification of benign and malignant lymph nodes are filtered to recognize pelvic lymph nodes. Improved FCM clustering algorithm is used to detect classification results of eight features achieved by cloud model analysis. Experiment results show that the clustering classification ability of eight features selected by cloud model can achieve an average recognition rate of 70% which is better than that of other features. So the eight features can be used for computer-aided classification and recognition system of pelvic lymph nodes.