Active contours driven by localizing region and edge-based intensity fitting energy with application to segmentation of the left ventricle in cardiac CT images

Zhou, Yan; Shi, Wei; Chen, Wei; Chen, Yong-lin; Liu, Ying; Tan, Liwen; Chen, Dai-Qiang

doi:10.1016/j.neucom.2014.12.061

Cited by 52 publications

(20 citation statements)

References 20 publications

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“…In medical imaging, segmentation helps extracting local information from the imaging data that can aid in clinical and diagnosis procedures [14]- [21]. State-ofthe-art segmentation techniques are usually formulated as an optimization problem, where the segmentation criteria and the contour characteristics are specified by an objective functional.…”

Section: Introductionmentioning

confidence: 99%

“…Although HAC has been shown to provide excellent segmentation results on medical images, it may perform poorly on images with several intensity inhomogeneities [44]. Zhou et al [21] propose to combine an edge-based active contour model and region-based active contour model for segmentation of the left ventricle in cardiac CT images. Based on the image gradient, their method adjusts the effect of the two models.…”

Section: Introductionmentioning

confidence: 99%

“…Although previously proposed methods also employ local features to control the contour's evolution [8], [21], [47], [48], they usually achieve this by incorporating additional energy terms and employing a set of empirically selected parameters to specify the influence of these terms. This may lead to inaccurate segmentation results, especially around weak edges.…”

Section: Introductionmentioning

confidence: 99%

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

Khadidos

Sánchez

2017

IEEE Trans. on Image Process.

162

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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...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Khadidos

Sánchez

2017

IEEE Trans. on Image Process.

162

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“…Wang et al [32] introduce the Local Chan-Vese (LCV) model, which employs both global image information and local statistics for efficiently segmenting images with intensity inhomogeneities. In [33], Zhou et al combine a local active contour model and an adaptive diffusion flow active contour model to improve medical segmentation in inhomogeneous regions with weak edges. In order to control the movement of the evolving contour towards the object's boundary in images with intensity inhomogeneities, Ji et al propose a fitting energy functional that minimizes a local likelihood energy term derived from the image intensity within each pixel's neighborhood [34].…”

Section: Introductionmentioning

confidence: 99%

Dual-Channel Active Contour Model for Megakaryocytic Cell Segmentation in Bone Marrow Trephine Histology Images

Song

Sánchez²,

EIDaly

et al. 2017

IEEE Trans. Biomed. Eng.

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“…But at the same time, the uneven gray scale of medical images in image segmentation using CV model is not obvious. Li Chunming [4][5] who proposed to refer to the point of the Gaussian kernel function, and the function as a local binary kernel function to fit the energy of local binary fitting model (LBF), solved the shortcomings of the CV model that contains the image of local information. Li Wang put forward LGIF model which combined with the advantages of the CV model and LBF model [6].…”

Section: Introductionmentioning

confidence: 99%

Extraction and Feature Analysis of Mouse Trabecular with Active Contour Model Based on Micro-CT Images

Chen¹,

Li²,

Chu³

2017

Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)

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Abstract. To solve the non-uniformity of micro-CT image with CV(Chan-Vese) model and the influence of location of initial contour curves on segmentation speed in the LGIF(Local and Global Intensity Fitting) model, K-LGIF(K-means-Local and Global Intensity Fitting) model was proposed through adding K-means clustering information into energy function of LGIF active contour model. The K-LGIF model extracts outline of the image as the initial contour to reduce the number of iterations and shorten time consuming. Comparing measured geometry parameters by simulating symptoms of osteoporosis and normal mouse femur of trabecular bone and using gray level co-occurrence matrix, we measured the parameters of texture distribution of trabecular bone. The experimental results show that the K-LGIF model can effectively improve segmentation of non-uniform gray image and increase speed of segmentation. This method may provide an approach for the quantitative analysis of osteoporosis.

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Active contours driven by localizing region and edge-based intensity fitting energy with application to segmentation of the left ventricle in cardiac CT images

Cited by 52 publications

References 20 publications

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

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

Dual-Channel Active Contour Model for Megakaryocytic Cell Segmentation in Bone Marrow Trephine Histology Images

Extraction and Feature Analysis of Mouse Trabecular with Active Contour Model Based on Micro-CT Images

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