{"title":"A novel segmentation algorithm for MRI brain tumor images","authors":"A. R. Abdulraqeb, W. Al-haidri, L. T. Sushkova","doi":"10.1109/USBEREIT.2018.8384535","DOIUrl":null,"url":null,"abstract":"A novel segmentation algorithm for MRI Brain tumor images is proposed. The proposed algorithm is compared with Thresholding and Region Grow methods. Testing was performed by generating two datasets of real MRI images of brain tumors. Criteria for assessment of the quality of the segmentation results were: the Dice score, sensitivity, specificity and accuracy. Analysis of results obtained using this algorithm to solve the brain tumor MRI image segmentation task showed levels of sensitivity and specificity of 91% to 99%, which is evidence that assessment of the position and boundaries of brain pathology is highly effective.","PeriodicalId":176222,"journal":{"name":"2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/USBEREIT.2018.8384535","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
A novel segmentation algorithm for MRI Brain tumor images is proposed. The proposed algorithm is compared with Thresholding and Region Grow methods. Testing was performed by generating two datasets of real MRI images of brain tumors. Criteria for assessment of the quality of the segmentation results were: the Dice score, sensitivity, specificity and accuracy. Analysis of results obtained using this algorithm to solve the brain tumor MRI image segmentation task showed levels of sensitivity and specificity of 91% to 99%, which is evidence that assessment of the position and boundaries of brain pathology is highly effective.
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