D. Nam, J. Mantell, Lorna Hodgson, D. Bull, P. Verkade, A. Achim
{"title":"基于特征的相关光学和电子显微镜图像配准","authors":"D. Nam, J. Mantell, Lorna Hodgson, D. Bull, P. Verkade, A. Achim","doi":"10.1109/ICIP.2014.7025724","DOIUrl":null,"url":null,"abstract":"In this paper we present a feature-based registration algorithm for largely misaligned bright-field light microscopy images and transmission electron microscopy images. We first detect cell centroids, using a gradient-based single-pass voting algorithm. Images are then aligned by finding the flip, translation and rotation parameters, which maximizes the overlap between pseudo-cell-centers. We demonstrate the effectiveness of our method, by comparing it to manually aligned images. Combining registered light and electron microscopy images together can reveal details about cellular structure with spatial and high-resolution information.","PeriodicalId":6856,"journal":{"name":"2014 IEEE International Conference on Image Processing (ICIP)","volume":"77 1","pages":"3567-3571"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Feature-based registration for correlative light and electron microscopy images\",\"authors\":\"D. Nam, J. Mantell, Lorna Hodgson, D. Bull, P. Verkade, A. Achim\",\"doi\":\"10.1109/ICIP.2014.7025724\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we present a feature-based registration algorithm for largely misaligned bright-field light microscopy images and transmission electron microscopy images. We first detect cell centroids, using a gradient-based single-pass voting algorithm. Images are then aligned by finding the flip, translation and rotation parameters, which maximizes the overlap between pseudo-cell-centers. We demonstrate the effectiveness of our method, by comparing it to manually aligned images. Combining registered light and electron microscopy images together can reveal details about cellular structure with spatial and high-resolution information.\",\"PeriodicalId\":6856,\"journal\":{\"name\":\"2014 IEEE International Conference on Image Processing (ICIP)\",\"volume\":\"77 1\",\"pages\":\"3567-3571\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE International Conference on Image Processing (ICIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIP.2014.7025724\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Conference on Image Processing (ICIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.2014.7025724","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Feature-based registration for correlative light and electron microscopy images
In this paper we present a feature-based registration algorithm for largely misaligned bright-field light microscopy images and transmission electron microscopy images. We first detect cell centroids, using a gradient-based single-pass voting algorithm. Images are then aligned by finding the flip, translation and rotation parameters, which maximizes the overlap between pseudo-cell-centers. We demonstrate the effectiveness of our method, by comparing it to manually aligned images. Combining registered light and electron microscopy images together can reveal details about cellular structure with spatial and high-resolution information.