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Proceedings Paper

The brain MRI classification problem from wavelets perspective
Author(s): Mohamed Mokhtar Bendib; Hayet Farida Merouani; Fatma Diaba
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Paper Abstract

Haar and Daubechies 4 (DB4) are the most used wavelets for brain MRI (Magnetic Resonance Imaging) classification. The former is simple and fast to compute while the latter is more complex and offers a better resolution. This paper explores the potential of both of them in performing Normal versus Pathological discrimination on the one hand, and Multiclassification on the other hand. The Whole Brain Atlas is used as a validation database, and the Random Forest (RF) algorithm is employed as a learning approach. The achieved results are discussed and statistically compared.

Paper Details

Date Published: 14 February 2015
PDF: 5 pages
Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 94451I (14 February 2015); doi: 10.1117/12.2180561
Show Author Affiliations
Mohamed Mokhtar Bendib, Badji-Mokhtar Univ. (Algeria)
LRI Lab. (Algeria)
Hayet Farida Merouani, Badji-Mokhtar Univ. (Algeria)
LRI Lab. (Algeria)
Fatma Diaba, Badji-Mokhtar Univ. (Algeria)
LMA Lab. (Algeria)


Published in SPIE Proceedings Vol. 9445:
Seventh International Conference on Machine Vision (ICMV 2014)
Antanas Verikas; Branislav Vuksanovic; Petia Radeva; Jianhong Zhou, Editor(s)

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