Hybrid diffusion-steered model for suppressing multiplicative noise in ultrasonograms

dc.creatorKessy, Suzan
dc.creatorMaiseli, Baraka
dc.creatorMichael, Kisangiri
dc.date2019-08-29T05:42:58Z
dc.date2019-08-29T05:42:58Z
dc.date2017-08
dc.date.accessioned2022-10-25T09:15:57Z
dc.date.available2022-10-25T09:15:57Z
dc.descriptionResearch Article published by An International Journal (SIPIJ) Vol.8, No.4, August 2017
dc.descriptionUltrasonograms refer to images generated through ultrasonography, a technique that applies ultrasound pulses to delineate internal structures of the body. Despite being useful in medicine, ultrasonograms usually suffer from multiplicative noises that may limit doctors to analyse and interpret them. Attempts to address the challenge have been made from previous works, but denoising ultrasonograms while preserving semantic features remains an open-ended problem. In this work, we have proposed a diffusion-steered model that gives an effective interplay between total variation and Perona-Malik models. Two parameters have been introduced into the framework to convexify our energy functional. Also, to deal with multiplicative noise, we have incorporated a log-based prior into the framework. Empirical results show that the proposed method generates sharper and detailed images. Even more importantly, our framework can be evolved over a longer time without smudging critical image features.
dc.formatapplication/pdf
dc.identifierDOI : 10.5121/sipij.2017.8401
dc.identifierhttp://dspace.nm-aist.ac.tz/handle/123456789/440
dc.identifier.urihttp://hdl.handle.net/123456789/94712
dc.publisherAn International Journal (SIPIJ)
dc.subjectultrasound image
dc.subjectPerona-Malik
dc.titleHybrid diffusion-steered model for suppressing multiplicative noise in ultrasonograms
dc.typeArticle

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