Color, texture, and shape are the common features used for the retrieval systems. However, many medical images have a spot of color information. Therefore, the discriminative texture and shape features should be extracted to obtain a satisfied retrieval result. In order to increase the credibility of the retrieval process, many features can be combined to be used for medical image retrieval. Meanwhile, more features require more processing time, which will decrease the retrieval speed. In this paper, wavelet decomposition is adopted to generate different resolution images. Bag-of-feature, texture, and LBP feature are extracted from three different-level wavelet images. Finally, the similarity measure function is obtained by fusing these three types of features. Experimental results show that the proposed multi-feature fusion method can achieve a higher retrieval accuracy with an acceptable retrieval time.

颜色，纹理和形状是用于检索系统的常见特征。但是，许多医学图像都有一些颜色信息。因此，应提取可辨别的纹理和形状特征以获得满意的检索结果。为了提高检索过程的可信度，可以将许多功能组合起来用于医学图像检索。同时，更多功能需要更多处理时间，这会降低检索速度。本文采用小波分解生成不同分辨率的图像。从三个不同级别的小波图像中提取特征包，纹理和LBP特征。最后，通过融合这三种类型的特征获得相似性度量函数。