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		<citationkey>SantosMasc:2018:StDiPa</citationkey>
		<title>Stochastic distances for patch-based ultrasound image despeckling</title>
		<format>On-line</format>
		<year>2018</year>
		<numberoffiles>1</numberoffiles>
		<size>292 KiB</size>
		<author>Santos, Cid Adinam Nogueira,</author>
		<author>Mascarenhas, Nelson Delfino DÁvila,</author>
		<affiliation>Universidade Federal de São Carlos</affiliation>
		<affiliation>Universidade Federal de São Carlos</affiliation>
		<editor>Ross, Arun,</editor>
		<editor>Gastal, Eduardo S. L.,</editor>
		<editor>Jorge, Joaquim A.,</editor>
		<editor>Queiroz, Ricardo L. de,</editor>
		<editor>Minetto, Rodrigo,</editor>
		<editor>Sarkar, Sudeep,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Oliveira, Manuel M.,</editor>
		<editor>Arbeláez, Pablo,</editor>
		<editor>Mery, Domingo,</editor>
		<editor>Oliveira, Maria Cristina Ferreira de,</editor>
		<editor>Spina, Thiago Vallin,</editor>
		<editor>Mendes, Caroline Mazetto,</editor>
		<editor>Costa, Henrique Sérgio Gutierrez,</editor>
		<editor>Mejail, Marta Estela,</editor>
		<editor>Geus, Klaus de,</editor>
		<editor>Scheer, Sergio,</editor>
		<e-mailaddress>santos.cid@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)</conferencename>
		<conferencelocation>Foz do Iguaçu, PR, Brazil</conferencelocation>
		<date>29 Oct.-1 Nov. 2018</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Master's or Doctoral Work</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>despeckling, ultrasound imaging, patch-based filtering, stochastic distances, geodesic distances, BM3D, NLM.</keywords>
		<abstract>Ultrasound image despeckling is an important research field since it can improve the interpretability of one of the main categories of medical imaging. Many techniques have been tried over the years for ultrasound despeckling, and more recently, a great deal of attention has been focused on patch-based methods, such as non-local means (NLM) and block-matching collaborative filtering (BM3D). A common idea in these recent methods is the measure of distance between patches, originally proposed as the Euclidean distance, for filtering additive white Gaussian noise. In this work, we derive several new similarity measures based on the statistics of the speckle and apply them for despeckling both radio frequency (RF) and log-compressed US signals. State-of-the-art results in filtering simulated, synthetic, and real ultrasound images confirm the potential of the proposed approach.</abstract>
		<language>en</language>
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		<usergroup>santos.cid@gmail.com</usergroup>
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