19 September 2017 Brain's tumor image processing using shearlet transform
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Abstract
Brain tumor detection is well known research area for medical and computer scientists. In last decades there has been much research done on tumor detection, segmentation, and classification. Medical imaging plays a central role in the diagnosis of brain tumors and nowadays uses methods non-invasive, high-resolution techniques, especially magnetic resonance imaging and computed tomography scans. Edge detection is a fundamental tool in image processing, particularly in the areas of feature detection and feature extraction, which aim at identifying points in a digital image at which the image has discontinuities. Shearlets is the most successful frameworks for the efficient representation of multidimensional data, capturing edges and other anisotropic features which frequently dominate multidimensional phenomena. The paper proposes an improved brain tumor detection method by automatically detecting tumor location in MR images, its features are extracted by new shearlet transform.
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Luis Cadena, Nikolai Espinosa, Franklin Cadena, Anna Korneeva, Alexey Kruglyakov, Alexander Legalov, Alexey Romanenko, Alexander Zotin, "Brain's tumor image processing using shearlet transform", Proc. SPIE 10396, Applications of Digital Image Processing XL, 103961B (19 September 2017); doi: 10.1117/12.2272792; https://doi.org/10.1117/12.2272792
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