摘要
利用灰度共生矩阵提取斑块特征,选取能量、熵、惯性矩和相关4种有效特征值组成特征向量;结合粒子群算法构造支持向量机分类器,并对基于高斯径向基核函数的支持向量机分类参数进行优化。实验结果表明本文方法所消耗的时间相对较少,对常见的4种斑块的平均识别正确率达到92%,验证了方法的有效性。
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%. It verifies the effectiveness of the proposed method.
出处
《数据采集与处理》
CSCD
北大核心
2012年第3期283-286,共4页
Journal of Data Acquisition and Processing
关键词
动脉硬化
斑块识别
纹理分析
特征提取
atherosclerotic
plaque recognition
texture analysis
feature extraction
