{"title":"基于Mallat-Zhong离散小波变换的超声图像各向异性扩散去斑方法","authors":"Wu Shibin, Chen Bo, D. Wangli, G. Xiaoming","doi":"10.3724/SP.J.1087.2013.03201","DOIUrl":null,"url":null,"abstract":"In view of speckle noise in ultrasound image, there are some disadvantages of traditional anisotropic diffusion methods, such as in-sufficient noise suppression and edge details preservation. A de-speckling method based on Mallat-Zhong Discrete Wavelet Transform( MZ-DWT) wavelet was proposed. The method used MZ-DWT wavelet and Expectation Maximization( EM) algorithm as the discrimination factor between homogeneous and edge regions, making it more accurately to control diffusion intensity and rate and achieving the noise suppression and details preservation. The experimental results show that, the proposed algorithm can better de-speckle while preserving image details and the performance of the method is better than the traditional anisotropic diffusion methods.","PeriodicalId":61778,"journal":{"name":"计算机应用","volume":"33 1","pages":"3201-3203"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ultrasound image anisotropic diffusion de-speckling method based on Mallat-Zhong discrete wavelet transform wavelet\",\"authors\":\"Wu Shibin, Chen Bo, D. Wangli, G. Xiaoming\",\"doi\":\"10.3724/SP.J.1087.2013.03201\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In view of speckle noise in ultrasound image, there are some disadvantages of traditional anisotropic diffusion methods, such as in-sufficient noise suppression and edge details preservation. A de-speckling method based on Mallat-Zhong Discrete Wavelet Transform( MZ-DWT) wavelet was proposed. The method used MZ-DWT wavelet and Expectation Maximization( EM) algorithm as the discrimination factor between homogeneous and edge regions, making it more accurately to control diffusion intensity and rate and achieving the noise suppression and details preservation. The experimental results show that, the proposed algorithm can better de-speckle while preserving image details and the performance of the method is better than the traditional anisotropic diffusion methods.\",\"PeriodicalId\":61778,\"journal\":{\"name\":\"计算机应用\",\"volume\":\"33 1\",\"pages\":\"3201-3203\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"计算机应用\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.3724/SP.J.1087.2013.03201\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"计算机应用","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.3724/SP.J.1087.2013.03201","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Ultrasound image anisotropic diffusion de-speckling method based on Mallat-Zhong discrete wavelet transform wavelet
In view of speckle noise in ultrasound image, there are some disadvantages of traditional anisotropic diffusion methods, such as in-sufficient noise suppression and edge details preservation. A de-speckling method based on Mallat-Zhong Discrete Wavelet Transform( MZ-DWT) wavelet was proposed. The method used MZ-DWT wavelet and Expectation Maximization( EM) algorithm as the discrimination factor between homogeneous and edge regions, making it more accurately to control diffusion intensity and rate and achieving the noise suppression and details preservation. The experimental results show that, the proposed algorithm can better de-speckle while preserving image details and the performance of the method is better than the traditional anisotropic diffusion methods.