Integrating the PSONN and Boltzmann function for feature selection and classification of Lymph Node in Ultrasound Images

• Main Authors: Cheng-Ting Lai, 賴鄭婷
• Other Authors: Chuan-Yu Chang
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/6kcuu9

碩士 === 國立雲林科技大學 === 資訊工程研究所 === 96 === A lymph node (LN) is a part of the lymphatic system that exists in human body and every apparatus. LN can resist virus and germs. There are many kinds of pathological change in LN. Metastatic is one of the important indexes in staging malignant tumors. One convenient tool to observe LN is the use of an ultrasonic image. Clinical physicians judge a nosology by pathological section and experience of the professionals. Shortcoming of this method is that it requires lots of precious time of clinical physicians. In engineer’s view, we can help with some technology to classify images took with ultrasound. In this paper, we propose a system that classifies Lymph Node with different pathological change in ultrasonic images. Features are selected as well as extracted from the ultrasonic images. Furthermore, a feature-selecting method that integrates the particle swarm optimization neural network (PSONN) with Boltzmann probabilistic and the support vector machine (SVM) neural network is adopted to classify these images. The experimental results show that the proposed approach decreases the number of the selected features and achieves a high accuracy in classification.