Multiscale geodesic active contours for ultrasound image segmentation using speckle reducing anisotropic diffusion

Wang, Weiming; Zhu, Lei; Qin, Jing; Chui, Yim Pan; Li, Bing Nan; Heng, Pheng‐Ann

doi:10.1016/j.optlaseng.2013.10.003

Cited by 61 publications

(40 citation statements)

References 41 publications

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“…According to our experiments, the global mean 1 c and 2 c is the classification center of the image gray, and their value are mainly influenced by the total inner region pixels instead of the edge pixels. Comparing to the inner gray distribution, the contour is more easily influenced by the edge pixels' gray.…”

Section: The Proposed Modelmentioning

confidence: 82%

Image Segmentation Framework Using Gradient Guided Active Contours

Cai¹,

Liu²,

Wang³

et al. 2015

IJSIP

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Section: The Proposed Modelmentioning

confidence: 82%

Image Segmentation Framework Using Gradient Guided Active Contours

Cai¹,

Liu²,

Wang³

et al. 2015

IJSIP

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“…The method SRAD (Speckle Reducing Anisotropic Diffusion) is applied to reduce speckle noise in ultrasound images [16]. Speckle noise is the grainy white and black texture in an image, which makes it hard to detect the edges of an object accurately.…”

Section: A Denoisingmentioning

confidence: 99%

“…To recognize the cytoplasm of cervical cells, a new method multi-way segmentation method is proposed by combining the graph-cut and thresholding [16]. Different from traditional graph based algorithm, the construction of graph by using this method uses the mean intensity values of 4 classes divided by thresholding.…”

Section: B Graph-based Segmentationmentioning

confidence: 99%

“…However, the authors were aware of the strengths and shortcoming of this method well, and they simply used this method to do the coarse detection both to save time and not affect accuracy. A similar application of threshold method is used in the segmentation on cytoplasm of cervical cell images [16]. In this application, the multiple thresholding is intended for initial segments.…”

Section: E Thresholdmentioning

confidence: 99%

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Current Status of Bio-medical Imaging Technology and Method in China: A Review

Liu¹,

Liu²,

Wang³

et al. 2014

BER

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Abstract-Medicine imaging plays a more and more important role in illness detection and treatment. Various imaging skills, including CT imaging, MR imaging, ultrasound imaging and so on are in application and under research nowadays, and those images could be got from medicine databases in hospitals, clinical cases or volunteers. Since the biomedical researches are attracting more and more attention in China, our group investigated papers on biomedical published in the last 3 years in China and referenced 35 papers here. From different views, we investigated methods applied in image resources, image preprocessing, image registration, image segmentation, and their application fields. By reviewing those papers, we aim at getting a brief view of the current biomedical research trend in China.

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“…In their approach, the authors construct a speed term based on two phase features: local phase, which is derived from the monogenic signal; and local orientation, which measures the alignment between the local image orientations and the contour's normal direction of movement. PBLS has shown to perform very well in the presence of weak edges, despite requiring a careful tuning of the parameters associated with the edge map used by the method [43]. Estellers et al [31] propose a segmentation method based on the geometric representation of images as 2D manifolds embedded in a higher dimensional space.…”

Section: Introductionmentioning

confidence: 99%

Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation

Khadidos

Sánchez

2017

IEEE Trans. on Image Process.

167

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Permanent WRAP URL:http://wrap.warwick.ac.uk86204 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way.Publisher's statement: "© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works." A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP URL' above for details on accessing the published version and note that access may require a subscription.For more information, please contact the WRAP Team at: wrap@warwick.ac.uk JOURNAL OF L A T E X CLASS FILES 1 Weighted Level Set Evolution Based on Local Edge Features for Medical Image SegmentationAlaa Khadidos, Victor Sanchez, Member, IEEE, and Chang-Tsun Li, Senior Member, IEEE Abstract-Level set methods have been widely used to implement active contours for image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may hinder the accuracy of any segmentation method based on active contours implemented using level set methods. This paper proposes a method based on active contours implemented using level set methods for segmentation of such medical images. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries. This relative importance is computed based on local edge features collected from the adjacent region located inside and outside of the evolving contour. The local edge features employed are the edge intensity and the degree of alignment between the image's gradient vector flow field and the evolving contour's normal. We evaluate the proposed method for segmentation of various regions in real MRI and CT slices, X-ray images, and ultra sound images. Evaluation results confirm the advantage of w...

show abstract

Multiscale geodesic active contours for ultrasound image segmentation using speckle reducing anisotropic diffusion

Cited by 61 publications

References 41 publications

Image Segmentation Framework Using Gradient Guided Active Contours

Image Segmentation Framework Using Gradient Guided Active Contours

Current Status of Bio-medical Imaging Technology and Method in China: A Review

Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation

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