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Brain's tumor image processing using shearlet transform
Author(s): Luis Cadena; Franklin Cadena; Nikolai Espinosa; Anna Korneeva; Alexey Kruglyakov; Alexander Legalov; Alexey Romanenko; Alexander Zotin
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Paper 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.

Paper Details

Date Published: 19 September 2017
PDF: 6 pages
Proc. SPIE 10396, Applications of Digital Image Processing XL, 103961B (19 September 2017); doi: 10.1117/12.2272792
Show Author Affiliations
Luis Cadena, Univ. de las Fuerzas Armadas ESPE (Ecuador)
Siberian State Aerospace Univ. (Russian Federation)
Franklin Cadena, College Juan Suarez Chacon (Ecuador)
Nikolai Espinosa, Univ. de las Fuerzas Armadas ESPE (Ecuador)
Anna Korneeva, Siberian Federal Univ. (Russian Federation)
Alexey Kruglyakov, Siberian Federal Univ. (Russian Federation)
Alexander Legalov, Siberian Federal Univ. (Russian Federation)
Alexey Romanenko, Novosibirsk State Univ. (Russian Federation)
Alexander Zotin, Siberian State Aerospace Univ. (Russian Federation)


Published in SPIE Proceedings Vol. 10396:
Applications of Digital Image Processing XL
Andrew G. Tescher, Editor(s)

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