{"title":"MRI视频成像的自动对比度增强","authors":"S. I. Jabbar, A. Aladi","doi":"10.1109/AICT47866.2019.8981719","DOIUrl":null,"url":null,"abstract":"Contrast enhancement of the Magnetic Resonance Imaging (MRI) videos is a powerful tool that helps to illustrate different essential details. In this work, a new automated video contrast enhancement method was introduced and was applied on the cardiac MRI video. This technique was based on fuzzy image technique, which consists of three stages: fuzzification, modification of membership equation, and defuzzification. In the second stage, the parameters of the membership function were selected depending on evaluation of maximum fuzzy entropy. The quality of results was evaluated by comparison with other methods using quantitative metrics. The results demonstrate an improved contrast of MRI video using this method, where the contrast is 50% more than input MRI video and 10% higher compared other method.","PeriodicalId":329473,"journal":{"name":"2019 IEEE 13th International Conference on Application of Information and Communication Technologies (AICT)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Automated Contrast Enhancement of the MRI Video Imaging\",\"authors\":\"S. I. Jabbar, A. Aladi\",\"doi\":\"10.1109/AICT47866.2019.8981719\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Contrast enhancement of the Magnetic Resonance Imaging (MRI) videos is a powerful tool that helps to illustrate different essential details. In this work, a new automated video contrast enhancement method was introduced and was applied on the cardiac MRI video. This technique was based on fuzzy image technique, which consists of three stages: fuzzification, modification of membership equation, and defuzzification. In the second stage, the parameters of the membership function were selected depending on evaluation of maximum fuzzy entropy. The quality of results was evaluated by comparison with other methods using quantitative metrics. The results demonstrate an improved contrast of MRI video using this method, where the contrast is 50% more than input MRI video and 10% higher compared other method.\",\"PeriodicalId\":329473,\"journal\":{\"name\":\"2019 IEEE 13th International Conference on Application of Information and Communication Technologies (AICT)\",\"volume\":\"64 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 13th International Conference on Application of Information and Communication Technologies (AICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AICT47866.2019.8981719\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 13th International Conference on Application of Information and Communication Technologies (AICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AICT47866.2019.8981719","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automated Contrast Enhancement of the MRI Video Imaging
Contrast enhancement of the Magnetic Resonance Imaging (MRI) videos is a powerful tool that helps to illustrate different essential details. In this work, a new automated video contrast enhancement method was introduced and was applied on the cardiac MRI video. This technique was based on fuzzy image technique, which consists of three stages: fuzzification, modification of membership equation, and defuzzification. In the second stage, the parameters of the membership function were selected depending on evaluation of maximum fuzzy entropy. The quality of results was evaluated by comparison with other methods using quantitative metrics. The results demonstrate an improved contrast of MRI video using this method, where the contrast is 50% more than input MRI video and 10% higher compared other method.