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Ultrasound Image Enhancement Using Structure-Based Filtering

Ultrasound images are prone to speckle noises. Speckles blur features which are essential for diagnosis and assessment. Thus despeckling is a necessity in ultrasound image processing. Linear filters can suppress speckles, but they smooth out features. Median filter based despeckling algorithms produ...

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Detalles Bibliográficos
Autores principales: Ueng, Shyh-Kuang, Yen, Cho-Li, Chen, Guan-Zhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4089207/
https://www.ncbi.nlm.nih.gov/pubmed/25110515
http://dx.doi.org/10.1155/2014/758439
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author Ueng, Shyh-Kuang
Yen, Cho-Li
Chen, Guan-Zhi
author_facet Ueng, Shyh-Kuang
Yen, Cho-Li
Chen, Guan-Zhi
author_sort Ueng, Shyh-Kuang
collection PubMed
description Ultrasound images are prone to speckle noises. Speckles blur features which are essential for diagnosis and assessment. Thus despeckling is a necessity in ultrasound image processing. Linear filters can suppress speckles, but they smooth out features. Median filter based despeckling algorithms produce better results. However, they may produce artifact patterns in the resulted images and oversmooth nonuniform regions. This paper presents an innovative despeckle procedure for ultrasound images. In the proposed method, the diffusion tensor of intensity is computed at each pixel at first. Then the eigensystem of the diffusion tensor is calculated and employed to detect and classify the underlying structure. Based on the classification result, a feasible filter is selected to suppress speckles and enhance features. Test results show that the proposed despeckle method reduces speckles in uniform areas and enhances tissue boundaries and spots.
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spelling pubmed-40892072014-08-10 Ultrasound Image Enhancement Using Structure-Based Filtering Ueng, Shyh-Kuang Yen, Cho-Li Chen, Guan-Zhi Comput Math Methods Med Research Article Ultrasound images are prone to speckle noises. Speckles blur features which are essential for diagnosis and assessment. Thus despeckling is a necessity in ultrasound image processing. Linear filters can suppress speckles, but they smooth out features. Median filter based despeckling algorithms produce better results. However, they may produce artifact patterns in the resulted images and oversmooth nonuniform regions. This paper presents an innovative despeckle procedure for ultrasound images. In the proposed method, the diffusion tensor of intensity is computed at each pixel at first. Then the eigensystem of the diffusion tensor is calculated and employed to detect and classify the underlying structure. Based on the classification result, a feasible filter is selected to suppress speckles and enhance features. Test results show that the proposed despeckle method reduces speckles in uniform areas and enhances tissue boundaries and spots. Hindawi Publishing Corporation 2014 2014-06-19 /pmc/articles/PMC4089207/ /pubmed/25110515 http://dx.doi.org/10.1155/2014/758439 Text en Copyright © 2014 Shyh-Kuang Ueng et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Ueng, Shyh-Kuang
Yen, Cho-Li
Chen, Guan-Zhi
Ultrasound Image Enhancement Using Structure-Based Filtering
title Ultrasound Image Enhancement Using Structure-Based Filtering
title_full Ultrasound Image Enhancement Using Structure-Based Filtering
title_fullStr Ultrasound Image Enhancement Using Structure-Based Filtering
title_full_unstemmed Ultrasound Image Enhancement Using Structure-Based Filtering
title_short Ultrasound Image Enhancement Using Structure-Based Filtering
title_sort ultrasound image enhancement using structure-based filtering
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4089207/
https://www.ncbi.nlm.nih.gov/pubmed/25110515
http://dx.doi.org/10.1155/2014/758439
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AT yencholi ultrasoundimageenhancementusingstructurebasedfiltering
AT chenguanzhi ultrasoundimageenhancementusingstructurebasedfiltering