Abstract: This paper presents a Content-Based Image Retrieval（CBIR） method that is used in medical CT images of liver lesions with a Computer-Assisted Diagnosis（CAD）.According to medical CT image characteristics of blurred boundaries and the unconspicuous region，the liver region of interest is extracted by using semi-automatic method.This paper extracts local co-occurrence matrix texture features and intensity features，and uses improved non-tensor product wavelet filter to extract the image global features.Experimental results show that this method can improve the detection rate of lesions.It obtains good results in hepatic hemangioma and HCC which are difficult differential diagnosis both of rich blood supply to tumors.

Key words: Content-Based Image Retrieval（CBIR）, liver CT image, non-tensor product wavelet filter banks, features extracting, distance measure

摘要： 提出了一种基于内容的图像检索（CBIR）方法，用于医学肝脏带病灶CT图像的计算机辅助诊断（CAD）。根据医学CT图像的模糊边界和区域特征不明显等特点，将肝部感兴趣区域用半自动方法分离出来，提取局部纹理共生矩阵特征和灰度特征，然后利用改进的非张量积小波滤波器组提取图像全局特征。实验结果表明，该方法可以提高病灶的检出率，对较难鉴别诊断肝血管瘤和肝癌这两种丰富供血肿瘤的CT图像问题，也有较好的效果。

关键词: 基于内容的图像检索（CBIR）, 肝脏CT图像, 非张量积小波滤波器组, 特征提取, 距离测度