{"title":"基于两阶段阈值和形态学图像恢复的Hessian血管分割改进","authors":"S. Mirhassani, M. Hosseini, A. Behrad","doi":"10.1109/IIT.2009.5413357","DOIUrl":null,"url":null,"abstract":"In many of vessel segmentation methods, Hessian based vessel enhancement filter as an efficient step is employed. In this paper, for segmentation of vessels, HBVF method is the first step of the algorithm. Afterward, to remove non-vessels from image, a high level threshold is applied to the filtered image. Since, as a result of threshold some of weak vessels are removed, recovering of vessels using Hough transform and morphological operations is accomplished. Then, the yielded image is combined with a version of vesselness filtered image which is converted to a binary image using a low level threshold. As a consequence of image combination, most of vessels are detected. In the final step, to reduce the false positives, fine particles are removed from the result according to their size. Experiments indicate the promising results which demonstrate the efficiency of the proposed algorithm.","PeriodicalId":239829,"journal":{"name":"2009 International Conference on Innovations in Information Technology (IIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Improvement of Hessian based vessel segmentation using two stage threshold and morphological image recovering\",\"authors\":\"S. Mirhassani, M. Hosseini, A. Behrad\",\"doi\":\"10.1109/IIT.2009.5413357\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In many of vessel segmentation methods, Hessian based vessel enhancement filter as an efficient step is employed. In this paper, for segmentation of vessels, HBVF method is the first step of the algorithm. Afterward, to remove non-vessels from image, a high level threshold is applied to the filtered image. Since, as a result of threshold some of weak vessels are removed, recovering of vessels using Hough transform and morphological operations is accomplished. Then, the yielded image is combined with a version of vesselness filtered image which is converted to a binary image using a low level threshold. As a consequence of image combination, most of vessels are detected. In the final step, to reduce the false positives, fine particles are removed from the result according to their size. Experiments indicate the promising results which demonstrate the efficiency of the proposed algorithm.\",\"PeriodicalId\":239829,\"journal\":{\"name\":\"2009 International Conference on Innovations in Information Technology (IIT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Innovations in Information Technology (IIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IIT.2009.5413357\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Innovations in Information Technology (IIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IIT.2009.5413357","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improvement of Hessian based vessel segmentation using two stage threshold and morphological image recovering
In many of vessel segmentation methods, Hessian based vessel enhancement filter as an efficient step is employed. In this paper, for segmentation of vessels, HBVF method is the first step of the algorithm. Afterward, to remove non-vessels from image, a high level threshold is applied to the filtered image. Since, as a result of threshold some of weak vessels are removed, recovering of vessels using Hough transform and morphological operations is accomplished. Then, the yielded image is combined with a version of vesselness filtered image which is converted to a binary image using a low level threshold. As a consequence of image combination, most of vessels are detected. In the final step, to reduce the false positives, fine particles are removed from the result according to their size. Experiments indicate the promising results which demonstrate the efficiency of the proposed algorithm.