¹Dept. of Computer Science & Technology, Shandong University of Finance and Economics, Jinan, China Digital Media Technology Key Lab of Shandong Province, Jinan, China
²Central Hospital of Binzhou, Binzhou, China

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Abstract—In the field of computer-aided diagnosis the topics of image retrieval is an important approach. According to the difference of retrieval technology, modeling spatial context (e.g., autocorrelation) is a key challenge in image classification and retrieval problems that arise in image regions. This work proposes a new approach to the retrieval of medical images from traditional Markov Random Field model. Contrasting with previous work, this method relies on coping with the ambiguity of spatial relative position concepts: a new definition of the geometric relationship between two objects in a fuzzy set framework is proposed. Furthermore, Fuzzy Attributed Relational Graphs (FARGs) are used in this framework, where each node represents an image object and each edge represents the relationship between two objects. The generalization performance of this approach is then compared with alternative models over the IRMA dataset. These experiments show that our method outperforms the traditional models, such as MRF, FGM, SVM e.g., in terms of several standard measures.

Index Terms—spatial context, spatial relative position, fuzzy set, Fuzzy Attributed Relational Graphs(FARGs).

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Cite: Hui Liu and Guochao Sun, "A New Method of Medical Image Retrieval for Computer-Aided Diagnosis," Journal of Software vol. 7, no. 6, pp. 1289-1295, 2012.