R. Zaouche, Ahror Belaid, Bassel Solaiman, D. Salem, S. Tliba
{"title":"Segmentation of low-grade gliomas based on the growing region and level sets techniques","authors":"R. Zaouche, Ahror Belaid, Bassel Solaiman, D. Salem, S. Tliba","doi":"10.1109/ATSIP.2018.8364479","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a novel semi-automatic segmentation method based on the local image properties. Its originality is twofold, the first stands on the intensity invariant of phase-local information for the purpose of low-grade gliomas segmentation in MR images. In a second time, a level set method driven is combined to growing region so as to improve tumor detection. Experiments were conducted on a set of medical images. A comparison between the obtained results and the manual segmentation collected from experts is performed. The preliminary results are interesting and encouraging.","PeriodicalId":332253,"journal":{"name":"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ATSIP.2018.8364479","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, we propose a novel semi-automatic segmentation method based on the local image properties. Its originality is twofold, the first stands on the intensity invariant of phase-local information for the purpose of low-grade gliomas segmentation in MR images. In a second time, a level set method driven is combined to growing region so as to improve tumor detection. Experiments were conducted on a set of medical images. A comparison between the obtained results and the manual segmentation collected from experts is performed. The preliminary results are interesting and encouraging.