{"title":"Study with RK4 & ANOVA the location of the tumor at the smallest time for multi-images","authors":"E. Kaouther, S. Khelil, S. Hammoum","doi":"10.1109/ICCVIA.2015.7351890","DOIUrl":null,"url":null,"abstract":"In this paper, we have translated a nonlinear model to linear one by used the numerical analysis with Runge-Kutta 4 modify (RK4). This method is the most popular, where the step size H is working to increase the lighting of the image compared with the original picture. After that, we passed to the statistical study for linear regression then for the analysis of variance, or more briefly “ANOVA technique”, where we have used the statistical study on the pathological image to detect the tumors of multi MRI images and extract the place of lesion by two ways: distribution of Gaussian curve (hypothesis test of h0) and directly on the pathological image then compared the result obtained for nonlinear model with linear one. The simulation program applied with Matlab.","PeriodicalId":419122,"journal":{"name":"International Conference on Computer Vision and Image Analysis Applications","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Computer Vision and Image Analysis Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCVIA.2015.7351890","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
In this paper, we have translated a nonlinear model to linear one by used the numerical analysis with Runge-Kutta 4 modify (RK4). This method is the most popular, where the step size H is working to increase the lighting of the image compared with the original picture. After that, we passed to the statistical study for linear regression then for the analysis of variance, or more briefly “ANOVA technique”, where we have used the statistical study on the pathological image to detect the tumors of multi MRI images and extract the place of lesion by two ways: distribution of Gaussian curve (hypothesis test of h0) and directly on the pathological image then compared the result obtained for nonlinear model with linear one. The simulation program applied with Matlab.
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