{"title":"基于改进高斯模糊隶属函数的超声图像混合降噪方法","authors":"Priyankar Biswas, K. K. Halder, Arnab Sarkar","doi":"10.1109/ICRPSET57982.2022.10188550","DOIUrl":null,"url":null,"abstract":"In biomedical imaging, most of the images are usually distorted by various forms of noises such as Gaussian noise, speckle noise, etc. Therefore, removing these noises from the medical images is required for proper diagnosis and better analysis of human inner organs, body tissues, and many more. A new strategy for suppressing combined Gaussian noise and speckle noise in medical ultrasound images has been proposed in this research. This methodology is founded on a fuzzy filter which follows a modified Gaussian membership function. The experimental findings show that the proposed filter is competent in reducing the combined noises, even with higher densities, and is competitive with the existing methods.","PeriodicalId":405673,"journal":{"name":"2022 International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET)","volume":"183 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modified Gaussian Fuzzy Membership Function for Mixed Noise Reduction from Ultrasound Images\",\"authors\":\"Priyankar Biswas, K. K. Halder, Arnab Sarkar\",\"doi\":\"10.1109/ICRPSET57982.2022.10188550\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In biomedical imaging, most of the images are usually distorted by various forms of noises such as Gaussian noise, speckle noise, etc. Therefore, removing these noises from the medical images is required for proper diagnosis and better analysis of human inner organs, body tissues, and many more. A new strategy for suppressing combined Gaussian noise and speckle noise in medical ultrasound images has been proposed in this research. This methodology is founded on a fuzzy filter which follows a modified Gaussian membership function. The experimental findings show that the proposed filter is competent in reducing the combined noises, even with higher densities, and is competitive with the existing methods.\",\"PeriodicalId\":405673,\"journal\":{\"name\":\"2022 International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET)\",\"volume\":\"183 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICRPSET57982.2022.10188550\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRPSET57982.2022.10188550","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modified Gaussian Fuzzy Membership Function for Mixed Noise Reduction from Ultrasound Images
In biomedical imaging, most of the images are usually distorted by various forms of noises such as Gaussian noise, speckle noise, etc. Therefore, removing these noises from the medical images is required for proper diagnosis and better analysis of human inner organs, body tissues, and many more. A new strategy for suppressing combined Gaussian noise and speckle noise in medical ultrasound images has been proposed in this research. This methodology is founded on a fuzzy filter which follows a modified Gaussian membership function. The experimental findings show that the proposed filter is competent in reducing the combined noises, even with higher densities, and is competitive with the existing methods.