NDLI: Applying the Particle Swarm Optimization and Boltzmann Function for Feature Selection and Classification of Lymph Node in Ultrasound Images

Content Provider	IEEE Xplore Digital Library
Author	Chuan-Yu Chang Cheng-Ting Lai Shao-Jer Chen
Copyright Year	2008
Description	Author affiliation: Inst. of Comput. Sci. & Inf. Eng., Nat. Yunlin Univ. of Sci. & Technol., Yunlin (Cheng-Ting Lai) \|\| Dept. of Med. Imaging, Buddhist Tzu Chi Gen. Hosp., Chiayi (Shao-Jer Chen) \|\| Dept. of Comput. & Commun. Eng., Nat. Yunlin Univ. of Sci. & Technol., Yunlin (Chuan-Yu Chang)
Abstract	A lymph node (LN), which can resist virus and germs, is part of the lymphatic system that exists in the human body and every apparatus inside it. There are many kinds of pathological changes in LN. Metastatic is one of the important indexes to estimate the stage of malignant tumors. One convenient tool to observe LN is the use olf ultrasonic images. Clinical physicians judge a nosology by biopsy and experience. Shortcoming of the method is that it requires lots of precious time of clinical physicians. In this paper, we propose a method that classifies lymph node with different pathological changes in ultrasonic images. Features are extracted and selected from ultrasonic images. A feature selection method, which integrates the particle swarm optimization neural network (PSONN) with Boltzmann probabilistic, is proposed. Then, a support vector machine (SVM) is adopted for Lymph node classification. Experimental results show that the proposed approach decreases the number of the selected features and achieves a high accuracy in classification.
Starting Page	55
Ending Page	60
File Size	716260
Page Count	6
File Format	PDF
ISBN	9780769533827
DOI	10.1109/ISDA.2008.255
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-26
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lymphatic system Humans Metastasis Support Vector Machine Particle swarm optimization Lymph nodes Support vector machines Feature Selection Pathology Ultrasonic imaging Support vector machine classification Resists Particle Swarm Optimization
Content Type	Text
Resource Type	Article

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