{"title":"超声心动图图像增强使用自适应分数阶导数","authors":"Ayesha Saadia, A. Rashdi","doi":"10.1109/SIPROCESS.2016.7888245","DOIUrl":null,"url":null,"abstract":"Medical ultrasound images are low contrast in nature. Information regarding tissues and other important structure is required by a physician to assess patient's health. Therefore image enhancement is a critical pre-processing task. In this paper a methodology based on emerging topic of fractional calculus is proposed. Proposed method is simple yet effective. In the proposed algorithm, input image is first divided into smooth, texture and edge regions using gradient magnitude of each pixel. Then appropriate order of fractional differential mask is selected to enhance each pixel. Proposed method is compared with state-of-the-art histogram equalization method and fixed-order fractional differential methods. Results are verified quantitatively and qualitatively. For quantitative analysis average gradient and entropy are used. Simulation results verify the effectiveness of proposed method.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"177 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Echocardiography image enhancement using adaptive fractional order derivatives\",\"authors\":\"Ayesha Saadia, A. Rashdi\",\"doi\":\"10.1109/SIPROCESS.2016.7888245\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Medical ultrasound images are low contrast in nature. Information regarding tissues and other important structure is required by a physician to assess patient's health. Therefore image enhancement is a critical pre-processing task. In this paper a methodology based on emerging topic of fractional calculus is proposed. Proposed method is simple yet effective. In the proposed algorithm, input image is first divided into smooth, texture and edge regions using gradient magnitude of each pixel. Then appropriate order of fractional differential mask is selected to enhance each pixel. Proposed method is compared with state-of-the-art histogram equalization method and fixed-order fractional differential methods. Results are verified quantitatively and qualitatively. For quantitative analysis average gradient and entropy are used. Simulation results verify the effectiveness of proposed method.\",\"PeriodicalId\":142802,\"journal\":{\"name\":\"2016 IEEE International Conference on Signal and Image Processing (ICSIP)\",\"volume\":\"177 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE International Conference on Signal and Image Processing (ICSIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIPROCESS.2016.7888245\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIPROCESS.2016.7888245","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Echocardiography image enhancement using adaptive fractional order derivatives
Medical ultrasound images are low contrast in nature. Information regarding tissues and other important structure is required by a physician to assess patient's health. Therefore image enhancement is a critical pre-processing task. In this paper a methodology based on emerging topic of fractional calculus is proposed. Proposed method is simple yet effective. In the proposed algorithm, input image is first divided into smooth, texture and edge regions using gradient magnitude of each pixel. Then appropriate order of fractional differential mask is selected to enhance each pixel. Proposed method is compared with state-of-the-art histogram equalization method and fixed-order fractional differential methods. Results are verified quantitatively and qualitatively. For quantitative analysis average gradient and entropy are used. Simulation results verify the effectiveness of proposed method.