Brain tumor classification using sparse coding and dictionary learning

Saif Dawood Salman Al-shaikhli, Michael Ying Yang, Bodo Rosenhahn

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

15 Citations (Scopus)

Abstract

Brain tumor classification is considered as one of the most challenging tasks in medical imaging. In this paper, a novel approach for multi-class brain tumor classification based on sparse coding and dictionary learning is proposed. We propose an individual (per-class) dictionary learning and sparse coding classification using K-SVD algorithm. This approach combines topological and texture features to build and learn a dictionary. Experimental results demonstrate that the sparse coding based classification outperforms other state-of-the-art methods.
Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing (ICIP)
Place of PublicationParis
Pages2774-2778
Number of pages5
ISBN (Electronic)978-1-4799-5751-4
DOIs
Publication statusPublished - Oct 2014
Externally publishedYes
EventIEEE International Conference on Image Processing, ICIP 2014 - Paris, France
Duration: 27 Oct 201430 Oct 2014
https://icip2014.wp.imt.fr/

Conference

ConferenceIEEE International Conference on Image Processing, ICIP 2014
Abbreviated titleICIP
CountryFrance
CityParis
Period27/10/1430/10/14
Internet address

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