新疆地方性肝包虫CT图像的灰度直方图特征提取与分析

 图像特征提取是图像识别、图像数据挖掘、基于内容的图像检索等工作的基础,是模式识别和分类中的关键问题。本文运用灰度直方图法提取新疆地方性肝包虫CT图像特征,对图像进行尺寸归一、去噪和增强的预处理,并对灰度直方图特征进行统计分析,用最大类间距法获取图像分类的主要特征,同时使用判别分析法对特征的分类能力进行评价。结果表明,灰度直方图法提取的特征在统计分析中存在差异,且提高图像分类的准确率,一定程度上有助于对肝包虫病CT图像进行分类和检索。

Feature Extraction and Analysis on CT Image of Xinjiang Local Liver Hydatid by Using Gray-scale Histograms

The feature extraction of images is a foundational work for image recognition, image data mining, and content-based image retrieval, and it is also the key issues of pattern recognition and classification. Feature extraction based on gray-scale histograms is a typical algorithm for the medical image feature extraction. For features of liver hydatid CT images that is extracted by using different gray-scale histograms are normalizing scale by uniform quantization, the noise is removed by using a median filter, the contrast is enhanced by limited adaptive histogram equalization; and then the gray-scale histograms is used to get the features of the image. The main features of the image classification are obtained by using statistical and maximum classification distance analysis on the histogram features, and then the classification ability of features is evaluated by discriminant analysis. The result shows that there is a certain discrepancy of statistical analysis for the features extracted by gray-scale histograms; features selected by maximum classification distance enhance the accuracy of image classification. This study would lay a solid foundation for the content-based medical image retrieval and the computer-aided diagnosis system to a certain extent.