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21 May 1999 Automatic segmentation of blood vessels from MR angiography volume data by using fuzzy logic technique
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This paper shows a novel medical image segmentation method applied to blood vessel segmentation from magnetic resonance angiography volume data. The principle idea of the method is fuzzy information granulation concept. The method consists of 2 parts: (1) quantization and feature extraction, (2) iterative fuzzy synthesis. In the first part, volume quantization is performed with watershed segmentation technique. Each quantum is represented by three features, vascularity, narrowness and histogram consistency. Using these features, we estimate the fuzzy degrees of each quantum for knowledge models about MRA volume data. In the second part, the method increases the fuzzy degrees by selectively synthesizing neighboring quantums. As a result, we obtain some synthesized quantums. We regard them as fuzzy granules and classify them into blood vessel or fat by evaluating the fuzzy degrees. In the experimental result, three dimensional images are generated using target maximum intensity projection (MIP) and surface shaded display. The comparison with conventional MIP images shows that the unclarity region in conventional images are clearly depict in our images. The qualitative evaluation done by a physician shows that our method can extract blood vessel region and that the results are useful to diagnose the cerebral diseases.
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Syoji Kobashi, Yutaka Hata, Yasuhiro Tokimoto, and Makato Ishikawa "Automatic segmentation of blood vessels from MR angiography volume data by using fuzzy logic technique", Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999);

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