A mapping modelling of visual feature and knowledge representation approach for Medical Image Retrieval

The ever-increasing numbers of medical images in digital format are generated in clinical practice every day. These images include high-resolution and temporal data of various modalities and which constitute an important source of anatomical and functional information for diagnosis of diseases, research and education. So how to manage these large data and utilize them effectively and efficiently possess significant technical challenges. Thus, the technique of Content-based Medical Image Retrieval (CBMIR) emerges as the times require. However, current CBMIR is not sufficient to capture the semantic content of an image and difficult to provide good results according to the predefined categories in the medical domain for less using the medical knowledge. Accordingly, in this paper a mapping modelling of visual feature and knowledge representation is proposed to approach for medical image retrieval. Firstly, the low-level fusion visual features are extracted based on intensity, texture, and their extended versions. Secondly, a set of disjoint semantic tokens with appearance in lung CT images is selected to define a vocabulary based on medical knowledge representation. Finally, a mapping modeling is investigated to associate low-level visual image features with their high-level semantic. Experiments are conducted with a medical image database consisting of 220 lung medical images obtained from the Heilongjiang Province Hospital. And the comparison of the retrieval results shows that the approach proposed in this paper is effective.

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