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A Novel Active Contour Model for Medical Image Segmentation

Journal of Shanghai Jiaotong university(Science) 2010年第5期15卷 549-555页

作者：付增良叶铭苏永琳林艳萍王成焘 Institute of Biomedical Manufacturing and Life Quality Engineering Shanghai Jiaotong University

A novel segmentation method for medical image with intensity inhomogeneity is *** the proposed active contour model,both region and gradient information are taken into *** former,i.e.,region-based fitting energy,draws upon the region information and guarantees the accurate extraction of inhomogeneous image's local *** latter,i.e.,an edge indicator,weights the length penalizing term to consider the gradient ***,signed distance penalizing term is also added to ensure accurate computation and avoid the time-consuming re-initialization of evolving level set *** for synthetic and real images demonstrate the feasibility and superiority of the proposed model.

关键词： image segmentation active contour intensity inhomogeneity region-based fitting energy edge indicator

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Active contour driven by local entropy energy function for segmentation and bias correction

Active contour driven by local entropy energy function for s...

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第25届中国控制与决策会议

作者： Gai Pan Liqun Gao Zhaohua Cui College of Information Science and Engineering Northeastern University Unit 65041

ISBN: (纸本)9781467355339

C-V model has poor segmentation of images with intensity inhomogeneity. To overcome that problem, a novel active contour driven by a local entropy energy function is proposed to segment images with intensity inhomogeneity and get bias corrected images. The main idea of this paper is entropy can measure the degree of intensity homogeneity and local intensity information is extracted due to a weight function. Simulation experiments of 3 images show: this method can deal with intensity inhomogeneity problem of C-V model, and has better adaptability to initial location of the contour curve.

关键词： C-V Model Active Contour Entropy intensity inhomogeneity

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Active Contour Model via Local and Global intensity Information for Image Segmentation 36

Active Contour Model via Local and Global Intensity Informat...

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第36届中国控制会议

作者： Shuai Yuan Patrice Monkam Siqi Li Haolin Song Feng Zhang Information and Control Engineering Faculty Shenyang Jianzhu University

ISBN: (纸本)9781538629185

Active contour models（ACM） have been proven to be the most promising model in solving the different problems encountered in image *** paper proposes a new region-based active contour model for level set formulation in which the energy function is formulated using both local and global intensity fitting *** generalized Gaussian distribution has been used as the kernel function of the local binary fitting *** evolution equation consists of three terms:the global term,the local term and the regularization *** have introduced the Laplace operator into the regularization term to regularize the level set function during its evolution process,which efficiently eliminates the costly re-initialization *** to the definition of local image intensities,the proposed model is able to deal with intensity *** proposed model has been successfully applied to many synthetic and real-world images and the experimental results clearly show some improvement on both efficiency and accuracy compared with some popular methods.

关键词： Image segmentation intensity inhomogeneity Generalized Gaussian Kernel function Level set method Active contour model

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A Fuzzy Cluster and Retinex Theory-based Variation Model for Inhomogenous Image Segmentation

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火力与指挥控制 2021年第10期46卷 39-46,53页

作者： SUN Meng-xuan LUO Qun-nv MIN Li-hua FENG Can Msc-erlectronic and Electrical Engineering Durham UniversityDurhamUK School of Science Nanjing University of Posts and TelecommunicationsNanjingChina North Information Control Research Academy Group Co. ltd.NanjingChina

A variational inhomogeneous image segmentation model based on fuzzy membership functions and Retinex theory is proposed by introducing the fuzzy membership *** existence of the solution of the proposed model is proved theoretically.A valid algorithm is designed to make numerical solution of the model under the framework of alternating *** last experimental results show that the model can make segmentation of the real image with intensity inhomogeneity effectively.

关键词： image segmentation intensity inhomogeneity fuzzy membership Retinex theory alter-nating minimization

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A new Region-based Active Contours Combined with the GAC Model

A new Region-based Active Contours Combined with the GAC Mod...

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第37届中国控制会议

作者： Bo Wu Shuyan Xu Yanpeng Feng Shuang Zhang Education Technology and Information Center Shenzhen Polytechnic

intensity inhomogeneities often cause considerable difficulties in the quantitative analysis of images, especially for magnetic resonance（MR） images. It is hard to segment such images by using traditional level set based upon segmentation models. In order to come over these difficulties, in view of the experience of the predecessors, we proposed a new region-based active contour model. This model uses adaptive region force and the object’s boundary information to segment images we greatly interested in. The region-based force has local information which can handle intensity inhomogeneity, the time-consumption is also optimistic. The GAC model plays a role which can help to get through the weak boundary. Experimental results for synthetic and real images demonstrate the superior performance of our model.

关键词： Image segmentation Adaptive region force Active contour model intensity inhomogeneity Time-consumption

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A regularized estimation method for MR inhomogeneity correction

A regularized estimation method for MR inhomogeneity correct...

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第26届中国控制与决策会议

作者： Zhaohui Li Qiang Ling Kaikai Song Feng Li University of Science and Technology of China

Magnetic resonance imaging is affected by intensity inhomogeneity, also known as the bias field, which is mainly caused by imperfections in the radio frequency coils or inhomogeneous coil sensitivities in the receiving coils. In this paper, we proposed a new method which automatically correct the bias field using a regularized *** method does not need extra scan devices or empirical data. This method includes minimizing a cost function containing both a data-fit term and a regularization term, which provide robust inhomogeneity estimates. The method choose a region in which the intensities are similar and then use a low-pass filter and a regularized polynomial fitting method to estimate the bias field. The method has been evaluated with both simulated data and real clinical data and the results indicate that this method achieves excellent performance in estimating the intensity inhomogeneities.

关键词： intensity inhomogeneity regularization polynomial fitting

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