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Classification of Brain Hemorrhages in MRI Using Naïve Bayes- Probabilistic Kernel Approach

Classification of Brain Hemorrhages in MRI Using Naïve Bayes- Probabilistic Kernel Approach

Nita Kakhandaki, Shrinivas B. Kulkarni, Ramesh K., Umakant P. Kulkarni
Copyright: © 2019 |Volume: 21 |Issue: 3 |Pages: 15
ISSN: 1548-7717|EISSN: 1548-7725|EISBN13: 9781522563884|DOI: 10.4018/JCIT.2019070104
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MLA

Kakhandaki, Nita, et al. "Classification of Brain Hemorrhages in MRI Using Naïve Bayes- Probabilistic Kernel Approach." JCIT vol.21, no.3 2019: pp.51-65. http://doi.org/10.4018/JCIT.2019070104

APA

Kakhandaki, N., Kulkarni, S. B., Ramesh K., & Kulkarni, U. P. (2019). Classification of Brain Hemorrhages in MRI Using Naïve Bayes- Probabilistic Kernel Approach. Journal of Cases on Information Technology (JCIT), 21(3), 51-65. http://doi.org/10.4018/JCIT.2019070104

Chicago

Kakhandaki, Nita, et al. "Classification of Brain Hemorrhages in MRI Using Naïve Bayes- Probabilistic Kernel Approach," Journal of Cases on Information Technology (JCIT) 21, no.3: 51-65. http://doi.org/10.4018/JCIT.2019070104

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Abstract

A brain hemorrhage is one type of stroke, which is caused due to artery burst in the brain, killing the brain cells due to bleeding. Therefore, to reduce the criticality among the patients, for treatment, the doctors depend on accurate reports on the location of hemorrhage. Magnetic resonance imaging (MRI) is one of the best imaging modality when functional and structural abnormalities need to be found. To aid the identification of presence of abnormality, a novel NB-PKC algorithm for effective recognition of brain hemorrhages in MRI is proposed. A series of preprocessing is done, then the image undergoes binary thresholding process for applying an image mask on the hemorrhage region. Then for segmentation a modified multi-level segmenting algorithm is applied, using minimal local binary pattern and GLCM, combined features are extracted and finally for classification a novel Naïve Bayes- Probabilistic Kernel Classification is applied. These techniques designed could accurately identify the position and classified whether the image had an abnormality or not and could reduce human errors.

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