Integrating PSONN and Boltzmann function for feature selection and classification of lymph nodes in ultrasound images

Authors:
Chuan-Yu Chang;Chih-Chin Lai;Cheng-Ting Lai;Shao-Jer Chen
Affiliations:
Department of Computer Science and Information Engineering, National Yunlin University of Science & Technology, Douliou, Yunlin, Taiwan;Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung, Taiwan;Department of Computer Science and Information Engineering, National Yunlin University of Science & Technology, Douliou, Yunlin, Taiwan;Department of Medical Imaging, Buddhist Tzu Chi General Hospital, Dalin, Chia-Yi, Taiwan
Venue:
Journal of Visual Communication and Image Representation
Year:
2013

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Abstract

Lymph nodes (LNs), part of the lymphatic system, are important in the proper functioning of the immune system. LN metastasis is an important index for staging malignant tumors. The present study proposes a system that classifies lymph nodes according to pathological change from ultrasound (US) images. Features are selected and extracted from the US images. A feature selection method that integrates the particle swarm optimization neural network (PSONN) with the Boltzmann function is proposed to select significant features. A multi-class support vector machine (SVM) is adopted to classify diseases of the LN in the region of interests (ROIs) of US images into six categories. The experimental results show that the proposed approach decreases the number of selected features and that its classification is highly accurate.