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首页> 外文期刊>Journal of visualization >Automatic classification of brain computed tomography images using wavelet-based statistical texture features
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Automatic classification of brain computed tomography images using wavelet-based statistical texture features

机译：使用基于小波的统计纹理特征对计算机断层扫描图像进行自动分类

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摘要

Automated and accurate classification of computed tomography (CT) images is an integral component of the analysis and interpretation of neuro imaging. In this paper, we present the wavelet-based statistical texture analysis method for the classification of brain tissues into normal, benign, malignant tumor of CT images. Comparative studies of texture analysis method are performed for the proposed texture analysis method and spatial gray level dependence matrix method (SGLDM). Our proposed system consists of five phases (ⅰ) image acquisition, (ⅱ) discrete wavelet decomposition (DWT), (ⅲ) feature extraction, (ⅳ) feature selection, and (ⅴ) analysis of extracted texture features by classifier. A wavelet-based statistical texture feature set is derived from two level discrete wavelet transformed approximation (low frequency part of the image) sub image. Genetic algorithm (GA) and principal component analysis (PCA) are used to select the optimal texture features from the set of extracted features. The support vector machine (SVM) is employed as a classifier. The results of SVM for the texture analysis methods are evaluated using statistical analysis and receiver operating characteristic (ROC) analysis. The experimental results show that the proposed system is able to achieve higher classification accuracy effectiveness as measured by sensitivity and specificity.
机译：自动和准确的计算机断层扫描（CT）图像分类是神经成像分析和解释的组成部分。在本文中，我们提出了基于小波的统计纹理分析方法，将脑组织分类为CT图像的正常，良性，恶性肿瘤。对所提出的纹理分析方法和空间灰度依赖矩阵方法（SGLDM）进行了纹理分析方法的比较研究。我们提出的系统包括五个阶段（ⅰ）图像采集，（ⅱ）离散小波分解（DWT），（ⅲ）特征提取，（ⅳ）特征选择和（ⅴ）通过分类器分析提取的纹理特征。基于小波的统计纹理特征集是从两级离散小波变换逼近（图像的低频部分）子图像中得出的。遗传算法（GA）和主成分分析（PCA）用于从提取的特征集中选择最佳纹理特征。支持向量机（SVM）被用作分类器。使用统计分析和接收器工作特性（ROC）分析评估用于纹理分析方法的SVM结果。实验结果表明，所提出的系统能够通过灵敏度和特异性来实现更高的分类精度有效性。

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