{"title":"Automatic Registration Of The Medical Images - T1- And T2-weighted MR Knee Images","authors":"P. Zarychta","doi":"10.1109/MIXDES.2006.1706684","DOIUrl":null,"url":null,"abstract":"This article shows a new method of the automatic registration of T1- and T2-weighted MR knee images. This method is based on the entropy and energy measures of fuzziness and can be used in localization process of cruciate ligament. First, two sequences (T1- and T2-weighted) are converted to a fuzzy representation. Then, the entropy and energy measures are employed in the NCC (normalized cross correlation) and GD (gradient difference) methods. The alignment based on energy and entropy fuzzy measures shows a significant improvement in comparison with the implementation of the original image","PeriodicalId":318768,"journal":{"name":"Proceedings of the International Conference Mixed Design of Integrated Circuits and System, 2006. MIXDES 2006.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the International Conference Mixed Design of Integrated Circuits and System, 2006. MIXDES 2006.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MIXDES.2006.1706684","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
This article shows a new method of the automatic registration of T1- and T2-weighted MR knee images. This method is based on the entropy and energy measures of fuzziness and can be used in localization process of cruciate ligament. First, two sequences (T1- and T2-weighted) are converted to a fuzzy representation. Then, the entropy and energy measures are employed in the NCC (normalized cross correlation) and GD (gradient difference) methods. The alignment based on energy and entropy fuzzy measures shows a significant improvement in comparison with the implementation of the original image
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