基于边缘检测的多类别医学图像分类方法
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Multi-class Medical Image Classification Approach Based on Edge Detection
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    摘要:

    针对乳腺X光医学图像多类分类精度普遍较低的问题,提出了一种基于边缘检测的医学图像多类分类新方法。首先对乳腺X光医学图像进行预处理包括图像去噪和图像增强,再通过边缘检测方法,获取乳腺X光医学图像中的肿块区域,对检测到的肿块区域使用灰度共生矩阵提取特征,对于提取到的特征,采用支持向量机(Support vector machine,SVM)的方法进行分类;对于检测不到肿块区域的乳腺X光医学图像可直接分类为无乳腺癌(即正常)类。实验结果表明,与传统的支持向量机多类分类算法相比,基于边缘检测的医学图像多类分类新方法在乳腺X光医学图像上具有更高的分类精度。

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    To improve the low accuracy of breast X-ray medical image multi-class classification, a new medical image multi-class classification method based on edge detection is proposed. The breast X-ray medical image is firstly preprocessed, including image denoising and enhancement. Tumor region in X-ray medical image can be acquired through edge detection algorithm. Feature selection on tumor is implemented using gray level co-occurrence matrix. This method uses support vector machine (SVM) to classify the medical image according to selected features. The X-ray medical image without any detected edge can be directly classified into the normal without breast cancer. The experiment result shows that the new medical image multi-class classification method based on edge detection has a higher precision than the traditional SVM multi-class classification algoritm on breast X-ray medical image. 

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沈健 蒋芸 张亚男 胡学伟.基于边缘检测的多类别医学图像分类方法[J].数据采集与处理,2016,31(5):1028-1034

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  • 在线发布日期: 2018-04-09