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 (SDM College of Engg. & Tech., Dharwad, India), Shrinivas B. Kulkarni (SDM College of Engg. & Tech., Dharwad, India), Ramesh K. (Karnataka State Women's University, Bijapur, India) and Umakant P. Kulkarni (SDM College of Engg. & Tech., Dharwad, India)
Copyright: © 2019 |Pages: 15
DOI: 10.4018/JCIT.2019070104

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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Literature Survey

In this section the overview of the existing concepts which led to this work is discussed. For the early detection of abnormal changes in tissues and organs, MRI is an important imaging technique. An automatic unsupervised segmentation method by integrating Dual tree complex wavelet transform with K-mean algorithm was proposed for Brain MR Image. The brain region was extracted by using expectation-maximization segmentation software (EMS), and intensity inhomogeneities were corrected. Finally, spatial constrained K-mean algorithm was proposed for automatic T1-weighted brain MR image segmentation. (Jingdan Zhang, 2014) The intensity inhomogeneities in medical images affect the efficiency of region-based image segmentation methods whereas the performance of level set segmentation gets affected by initialization and configuration of controlling parameters. Hence, to address this a hybrid method which integrates a local region-based level set method with a variation of fuzzy clustering was proposed. (Marayam Rastgarpour, 2014) The hemorrhage identification is difficult when the area of blood is small or clinician is not experienced. Therefore, for automatic hemorrhage segmentation many researchers have been working. A hybrid method for detecting brain hemorrhage regions from clinical head CT scans was implemented. This method could detect ICH and higher attenuation signal caused by subarachnoid hemorrhage (SAH) or intra-ventricle hemorrhage (IVH). (Yonghong Li, 2009)

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