Image Processing

Etiology-based classification of brain white matter hyperintensity on magnetic resonance imaging

[+] Author Affiliations
Mariana Leite, Letícia Rittner, Roberto Lotufo

University of Campinas, Faculty of Electrical and Computer Engineering, Department of Computer Engineering and Industrial Automation, Albert Einstein Avenue, 13083-852 Campinas, Brazil

Simone Appenzeller

University of Campinas, Faculty of Medical Science, Rheumatology Division, Zeferino Vaz Avenue, 13083-970 Campinas, Brazil

Heloísa Helena Ruocco

Hospital of Faculty of Medicine of Jundiaí, Multiple Sclerosis Center, Francisco Telles Street, 13202-550 Jundiaí, Brazil

J. Med. Imag. 2(1), 014002 (Feb 19, 2015). doi:10.1117/1.JMI.2.1.014002
History: Received July 22, 2014; Accepted January 20, 2015
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Abstract.  Brain white matter lesions found upon magnetic resonance imaging are often observed in psychiatric or neurological patients. Individuals with these lesions present a more significant cognitive impairment when compared with individuals without them. We propose a computerized method to distinguish tissue containing white matter lesions of different etiologies (e.g., demyelinating or ischemic) using texture-based classifiers. Texture attributes were extracted from manually selected regions of interest and used to train and test supervised classifiers. Experiments were conducted to evaluate texture attribute discrimination and classifiers’ performances. The most discriminating texture attributes were obtained from the gray-level histogram and from the co-occurrence matrix. The best classifier was the support vector machine, which achieved an accuracy of 87.9% in distinguishing lesions with different etiologies and an accuracy of 99.29% in distinguishing normal white matter from white matter lesions.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Mariana Leite ; Letícia Rittner ; Simone Appenzeller ; Heloísa Helena Ruocco and Roberto Lotufo
"Etiology-based classification of brain white matter hyperintensity on magnetic resonance imaging", J. Med. Imag. 2(1), 014002 (Feb 19, 2015). ; http://dx.doi.org/10.1117/1.JMI.2.1.014002


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