数据采集与处理2012，Vol.27Issue(3)：283-286,4.
基于支持向量机的动脉硬化斑块识别
Recognition of Atherosclerotic Plaque Based on Support Vector Machine
汪友生 1胡百乐 1张丽杰 1吴焕焕 1王志东 1李亦林 1陈建新1
作者信息
- 1. 北京工业大学电子信息与控制工程学院,北京,100124
- 折叠
摘要
Abstract
Characteristics of plaque are extracted by the gray level co-occurrence matrix. The four effective characteristic values including energy, entropy, moment of inertia and correlation are selected to compose the eigenvector. And then support vector machine (SVM) is applied to construct the classifier combining particle swarm optimization (PSO) algorithm. The parameters of SVM are optimized based on Gaussian radius basis kernel function. The result shows that the method spends less time and the average recognition accuracy rate of the four common plaques reaches 92% At verifies the effectiveness of the proposed method.关键词
动脉硬化/斑块识别/纹理分析/特征提取Key words
atherosclerotic/ plaque recognition / texture analysis / feature extraction
分类
信息技术与安全科学引用本文复制引用
汪友生,胡百乐,张丽杰,吴焕焕,王志东,李亦林,陈建新..基于支持向量机的动脉硬化斑块识别[J].数据采集与处理,2012,27(3):283-286,4.
