{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:52:10Z","timestamp":1781110330399,"version":"3.54.1"},"reference-count":25,"publisher":"IGI Global Scientific Publishing","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,7,1]]},"abstract":"<p>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 Na\u00efve 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.<\/p>","DOI":"10.4018\/jcit.2019070104","type":"journal-article","created":{"date-parts":[[2019,4,23]],"date-time":"2019-04-23T09:03:57Z","timestamp":1556010237000},"page":"51-65","source":"Crossref","is-referenced-by-count":4,"title":["Classification of Brain Hemorrhages in MRI Using Na\u00efve Bayes- Probabilistic Kernel Approach"],"prefix":"10.4018","volume":"21","author":[{"given":"Nita","family":"Kakhandaki","sequence":"first","affiliation":[{"name":"SDM College of Engg. & Tech., Dharwad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shrinivas B","family":"Kulkarni","sequence":"additional","affiliation":[{"name":"SDM College of Engg. & Tech., Dharwad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Ramesh K.","sequence":"additional","affiliation":[{"name":"Karnataka State Women's University, Bijapur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umakant P","family":"Kulkarni","sequence":"additional","affiliation":[{"name":"SDM College of Engg. & Tech., Dharwad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"JCIT.2019070104-0","first-page":"9","article-title":"Tumor diagnosis in MRI brain image using ACM segmentation and ANN-LM classification techniques.","author":"A.Shenbagarajan","year":"2016","journal-title":"Indian Journal of Science and Technology"},{"key":"JCIT.2019070104-1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cvi.2014.0193"},{"key":"JCIT.2019070104-2","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2013.041105"},{"key":"JCIT.2019070104-3","article-title":"Automatic Brain Hemorrhage Segemntation and Classification in CT Scan Images.","author":"H. 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