G. Montilla, V. Barrios, V. Subacius, N. Rangel, C. Roux
{"title":"Model-based epicardial boundary detection using genetic algorithms","authors":"G. Montilla, V. Barrios, V. Subacius, N. Rangel, C. Roux","doi":"10.1109/IEMBS.1993.978514","DOIUrl":null,"url":null,"abstract":"AbsfrucfAn automutic mi>del-hussd melhod has been developed for thedetection ottheepiurdiill lwtiindury in purusternal short-axis hi-dimensionul cchtwrdit~rums, using genetic algorithms (GA) for optimirution. Our msthcd minimhe a glohd border dependent energy functitrn, which considem optimul edge detection and medical knowledge of the hourt. 'The shupe of the ventricular houndory Is modcled by U pnrnmelricellipticul Fourler series and the C A guides the ellipsdd shupe and position toword an optimal recognition. The mujor advantage of this mudel-based rcppronch, is that it correctly interpreg noise und incomplete images, which ore churncleristics of ccho imuges.","PeriodicalId":408657,"journal":{"name":"Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ","volume":"84 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1993-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEMBS.1993.978514","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
AbsfrucfAn automutic mi>del-hussd melhod has been developed for thedetection ottheepiurdiill lwtiindury in purusternal short-axis hi-dimensionul cchtwrdit~rums, using genetic algorithms (GA) for optimirution. Our msthcd minimhe a glohd border dependent energy functitrn, which considem optimul edge detection and medical knowledge of the hourt. 'The shupe of the ventricular houndory Is modcled by U pnrnmelricellipticul Fourler series and the C A guides the ellipsdd shupe and position toword an optimal recognition. The mujor advantage of this mudel-based rcppronch, is that it correctly interpreg noise und incomplete images, which ore churncleristics of ccho imuges.
摘要:提出了一种基于遗传算法优化的胸廓短轴高维尿径检测方法，用于胸廓外尿径检测。我们的模型最小化了一个全局边界依赖的能量函数，它考虑了最佳的边缘检测和小时的医学知识。采用U - p - p -椭圆傅勒级数对心室的形状进行建模，并利用C - A对椭圆的形状和位置进行最优识别。这种基于模型的方法的主要优点是能够正确地解释噪声和不完整图像，从而提高了图像的识别能力。
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