Brain's tumor image processing using shearlet transform

Luis Cadena, Nikolai Espinosa, Franklin Cadena, Anna Korneeva, Alexey Kruglyakov, Alexander Legalov, Alexey Romanenko, Alexander Zotin

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationApplications of Digital Image Processing XL
EditorsAG Tescher
Number of pages6
ISBN (Electronic)9781510612495
ISBN (Print)978-1-5106-1249-5
Publication statusPublished - 2017
EventApplications of Digital Image Processing XL 2017 - San Diego, United States
Duration: 7 Aug 201710 Aug 2017

Publication series

NameProceedings of SPIE
ISSN (Print)0277-786X


ConferenceApplications of Digital Image Processing XL 2017
CountryUnited States
CitySan Diego


  • brain tumor detection
  • edge detection
  • image analysis
  • Medical imaging
  • shearlet transform

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