The brain MRI classification problem from wavelets perspective

Mohamed Mokhtar Bendib; Hayet Farida Merouani; Fatma Diaba

doi:10.1117/12.2180561

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14 February 2015 The brain MRI classification problem from wavelets perspective

Mohamed Mokhtar Bendib, Hayet Farida Merouani, Fatma Diaba

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Proceedings Volume 9445, Seventh International Conference on Machine Vision (ICMV 2014); 94451I (2015) https://doi.org/10.1117/12.2180561
Event: Seventh International Conference on Machine Vision (ICMV 2014), 2014, Milan, Italy

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.

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Mohamed Mokhtar Bendib, Hayet Farida Merouani, and Fatma Diaba "The brain MRI classification problem from wavelets perspective", Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 94451I (14 February 2015); https://doi.org/10.1117/12.2180561

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