Multi-feature fusion method for medical image retrieval using wavelet and bag-of-features

• Main Authors: Liu Shuang, Chen Deyun, Chen Zhifeng, Pang Ming
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2019-10-01
• Series: Computer Assisted Surgery
• Subjects: Word; medical image retrieval; bag-of-feature; texture feature; LBP feature
• Online Access: http://dx.doi.org/10.1080/24699322.2018.1560087

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.