Automatic brain MR images diagnosis based on edge fractal dimension and spectral energy signature

Author

Lahmiri, Salim ; Boukadoum, Mounir

Author_Institution

Dept. of Comput. Sci., Univ. of Quebec at Montreal, Montreal, QC, Canada

fYear

2012

fDate

Aug. 28 2012-Sept. 1 2012

Firstpage

6243

Lastpage

6246

Abstract

A new automatic system to detect pathologies in human brain magnetic resonance (MR) images is presented. The goal is to classify normal versus abnormal images affected by Alzheimer, Glioma, Herpes, Metastatic, and Multiple Sclerosis. The extracted features are the fractal dimension of edges in the Hilbert domain, and the skewness and kurtosis of their spectral energy distribution. The proposed system (FDSE) outperforms the popular discrete wavelet transform (DWT) and principal component analysis (PCA).

Keywords

Hilbert transforms; biomedical MRI; brain; diseases; edge detection; feature extraction; image classification; medical image processing; Alzheimer disease; FDSE; Hilbert domain; automatic brain MR image diagnosis; automatic system; edge fractal dimension; feature extraction; glioma; herpes; human brain magnetic resonance image; metastasis; multiple sclerosis; pathology detection; spectral energy distribution kurtosis; spectral energy distribution skewness; spectral energy signature; Discrete wavelet transforms; Feature extraction; Fractals; Image edge detection; Pathology; Principal component analysis; Automation; Brain Diseases; Fractals; Humans; Magnetic Resonance Imaging;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE

Conference_Location

San Diego, CA

ISSN

1557-170X

Print_ISBN

978-1-4244-4119-8

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/EMBC.2012.6347421

Filename

6347421