Jiandong Fang, S. Fang, Jeffrey Huang, M. Tuceryan
{"title":"用于医学诊断的数字几何图像分析","authors":"Jiandong Fang, S. Fang, Jeffrey Huang, M. Tuceryan","doi":"10.1145/1141277.1141327","DOIUrl":null,"url":null,"abstract":"This paper describes a new medical image analysis technique for polygon mesh surfaces of human faces for a medical diagnosis application. The goal is to explore the natural patterns and 3D facial features to provide diagnostic information for Fetal Alcohol Syndrome (FAS). Our approach is based on a digital geometry analysis framework that applies pattern recognition techniques to digital geometry (polygon mesh) data from 3D laser scanners and other sources. Novel 3D geometric features are extracted and analyzed to determine the most discriminatory features that best represent FAS characteristics. As part of the NIH Consortium for FASD, the techniques developed here are being applied and tested on real patient datasets collected by the NIH Consortium both within and outside the US.","PeriodicalId":269830,"journal":{"name":"Proceedings of the 2006 ACM symposium on Applied computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2006-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Digital geometry image analysis for medical diagnosis\",\"authors\":\"Jiandong Fang, S. Fang, Jeffrey Huang, M. Tuceryan\",\"doi\":\"10.1145/1141277.1141327\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes a new medical image analysis technique for polygon mesh surfaces of human faces for a medical diagnosis application. The goal is to explore the natural patterns and 3D facial features to provide diagnostic information for Fetal Alcohol Syndrome (FAS). Our approach is based on a digital geometry analysis framework that applies pattern recognition techniques to digital geometry (polygon mesh) data from 3D laser scanners and other sources. Novel 3D geometric features are extracted and analyzed to determine the most discriminatory features that best represent FAS characteristics. As part of the NIH Consortium for FASD, the techniques developed here are being applied and tested on real patient datasets collected by the NIH Consortium both within and outside the US.\",\"PeriodicalId\":269830,\"journal\":{\"name\":\"Proceedings of the 2006 ACM symposium on Applied computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2006 ACM symposium on Applied computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1141277.1141327\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2006 ACM symposium on Applied computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1141277.1141327","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Digital geometry image analysis for medical diagnosis
This paper describes a new medical image analysis technique for polygon mesh surfaces of human faces for a medical diagnosis application. The goal is to explore the natural patterns and 3D facial features to provide diagnostic information for Fetal Alcohol Syndrome (FAS). Our approach is based on a digital geometry analysis framework that applies pattern recognition techniques to digital geometry (polygon mesh) data from 3D laser scanners and other sources. Novel 3D geometric features are extracted and analyzed to determine the most discriminatory features that best represent FAS characteristics. As part of the NIH Consortium for FASD, the techniques developed here are being applied and tested on real patient datasets collected by the NIH Consortium both within and outside the US.