{"title":"医学图像分割的形态学新技术","authors":"Priya, Vivek Singh Verma","doi":"10.1109/CIACT.2017.7977282","DOIUrl":null,"url":null,"abstract":"Image segmentation is a most important operation over an image for analysis or identification purposes. Segmentation implies clear distinct delineation between object of interest and rest of the image data known as background. Segmentation algorithm is generally based on either finding the discontinuity or similarity of pixels in the local neighborhood, they are commonly termed region-based or boundary based. Most of the times, these algorithms do not produce accurate segmentation. In this paper, medical Image segmentation based on morphological operators along with threshold selection is presented. The main aim is to have better segmentation accuracy and clarity of segmented image since medical images are sensitive images.","PeriodicalId":218079,"journal":{"name":"2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"New morphological technique for medical image segmentation\",\"authors\":\"Priya, Vivek Singh Verma\",\"doi\":\"10.1109/CIACT.2017.7977282\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Image segmentation is a most important operation over an image for analysis or identification purposes. Segmentation implies clear distinct delineation between object of interest and rest of the image data known as background. Segmentation algorithm is generally based on either finding the discontinuity or similarity of pixels in the local neighborhood, they are commonly termed region-based or boundary based. Most of the times, these algorithms do not produce accurate segmentation. In this paper, medical Image segmentation based on morphological operators along with threshold selection is presented. The main aim is to have better segmentation accuracy and clarity of segmented image since medical images are sensitive images.\",\"PeriodicalId\":218079,\"journal\":{\"name\":\"2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIACT.2017.7977282\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIACT.2017.7977282","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
New morphological technique for medical image segmentation
Image segmentation is a most important operation over an image for analysis or identification purposes. Segmentation implies clear distinct delineation between object of interest and rest of the image data known as background. Segmentation algorithm is generally based on either finding the discontinuity or similarity of pixels in the local neighborhood, they are commonly termed region-based or boundary based. Most of the times, these algorithms do not produce accurate segmentation. In this paper, medical Image segmentation based on morphological operators along with threshold selection is presented. The main aim is to have better segmentation accuracy and clarity of segmented image since medical images are sensitive images.