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Hybrid diffusion-steered model for suppressing multiplicative noise in ultrasonograms

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dc.contributor.author Kessy, Suzan
dc.contributor.author Maiseli, Baraka
dc.contributor.author Michael, Kisangiri
dc.date.accessioned 2019-08-29T05:42:58Z
dc.date.available 2019-08-29T05:42:58Z
dc.date.issued 2017-08
dc.identifier.uri DOI : 10.5121/sipij.2017.8401
dc.identifier.uri http://dspace.nm-aist.ac.tz/handle/123456789/440
dc.description Research Article published by An International Journal (SIPIJ) Vol.8, No.4, August 2017 en_US
dc.description.abstract Ultrasonograms 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. en_US
dc.publisher An International Journal (SIPIJ) en_US
dc.subject ultrasound image en_US
dc.subject Perona-Malik en_US
dc.title Hybrid diffusion-steered model for suppressing multiplicative noise in ultrasonograms en_US
dc.type Article en_US


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