Rim Rahali, Yassine Ben Salem, Noura Dridi, H. Dahman
{"title":"果蝇图像分割的b样条水平集","authors":"Rim Rahali, Yassine Ben Salem, Noura Dridi, H. Dahman","doi":"10.1109/ICIP40778.2020.9191177","DOIUrl":null,"url":null,"abstract":"Segmentation of biological images is a challenging task, due to non convex shapes, intensity inhomogeneity and clustered cells. To address these issues, a new algorithm is proposed based on the B-spline level set method. The implicit function of the level set is modelled as a continuous parametric function represented with the B-spline basis. It is different from the discrete formulation associated with conventional level set. In this paper the proposed framework takes into account properties of biological images. The algorithm is applied to Drosophila images, and compared to conventional level set and Marker Controlled Watershed (MCW). Results show good performance in term of the DICE coefficient, for noisy and noiseless images.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"B-Spline Level Set For Drosophila Image Segmentation\",\"authors\":\"Rim Rahali, Yassine Ben Salem, Noura Dridi, H. Dahman\",\"doi\":\"10.1109/ICIP40778.2020.9191177\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Segmentation of biological images is a challenging task, due to non convex shapes, intensity inhomogeneity and clustered cells. To address these issues, a new algorithm is proposed based on the B-spline level set method. The implicit function of the level set is modelled as a continuous parametric function represented with the B-spline basis. It is different from the discrete formulation associated with conventional level set. In this paper the proposed framework takes into account properties of biological images. The algorithm is applied to Drosophila images, and compared to conventional level set and Marker Controlled Watershed (MCW). Results show good performance in term of the DICE coefficient, for noisy and noiseless images.\",\"PeriodicalId\":405734,\"journal\":{\"name\":\"2020 IEEE International Conference on Image Processing (ICIP)\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Image Processing (ICIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIP40778.2020.9191177\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Image Processing (ICIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP40778.2020.9191177","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
B-Spline Level Set For Drosophila Image Segmentation
Segmentation of biological images is a challenging task, due to non convex shapes, intensity inhomogeneity and clustered cells. To address these issues, a new algorithm is proposed based on the B-spline level set method. The implicit function of the level set is modelled as a continuous parametric function represented with the B-spline basis. It is different from the discrete formulation associated with conventional level set. In this paper the proposed framework takes into account properties of biological images. The algorithm is applied to Drosophila images, and compared to conventional level set and Marker Controlled Watershed (MCW). Results show good performance in term of the DICE coefficient, for noisy and noiseless images.