医学图像病变区域的识别与仿真分析

Identification and Simulation Analysis of Medical Image Lesion Area

医学图像病变区识别对于提高疾病诊断准确性具有重要的作用,为了提高医学图像病变区域识别的正确率,提出了一种最小二乘支持向量机的医学图像病变区域识别方法。该方法将病变区域识别看作一个模式识别问题,利用灰度共生矩阵提取医学图像的特征,然后构建最小二乘支持向量机学习的数据,并利用最小二乘支持向量机良好的分类性能,构建医学图像病变区域分类器,实现病变区域快速识别,最后胶囊内窥镜图像为例分析其性能。结果表明,最小二乘支持向量机提高了医学图像病变区域的识别率,具有较高的实际应用价值,而且识别性能优于其它医学图像病变区域识别方法。

Medical image lesion area recognition is important to improve the accuracy of disease diagnosis,a new method for medical image lesion area recognition based on least squares support vector machine is proposed to improve the diagnostic accuracy of the disease.image lesion area recognition is considered as a pattern recognition problem,and the features of medical images are extracted by gray level co-occurrence matrix,and then data of least squares support vector machine learning is obtained and least squares support vector machine is used to build medical image lesion area classifier to achieve rapid identification of disease area base on its classification performance,finally,the performance of capsule endoscope image is analyzed as example.The results show that the least squares support vector machine can improve the recognition rate of medical image lesion area,has high application value,and the recognition performance is superior to other medical image recognition method.