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Fast and Accurate active contours for Object Boundary Segmentation

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Fast and Accurate Active Contours for Object Boundary Segmen...

Efficient Segmentation Approach for Different Medical Image Modalities

Electronic Communication Systems--The 2000 IEEE Asia-Pacific Conference on Circuits and Systems

作者： Cheng-Hung Chuang Wen-Nung Lie Dept.of Electrical Engineering National Chung Cheng University

ISBN: (纸本)0780362535

＜正＞We propose a new scheme of active contours for object boundary segmentation.The scheme is based on a greedy search algorithm over a gradient vector flow（GVF） field. This method is less sensitivity to initial contour conditions and accurate in approaching concave parts of object boundary.The basic ideas are to propagate edge information of object boundaries into a more spread areas near the initial contours by utilizing the GVF field and to speed up processing by applying a greedy search algorithm.A preprocessing procedure is also proposed to enhance the true gradient information in noisy images. Some artificial and real images are used for simulations and analysis.The results show accurate precision and fast speed for object boundary extraction.

关键词： image segmentation feature extraction gradient methods search problems active contours boundary segmentation gradient vector flow initial contour conditions concave parts object boundary edge information GVF field greedy search algorithm preprocessing gradient information noisy images artificial images real images simulation object boundary extraction

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Computers, Materials & Continua 2022年第11期73卷 3119-3135页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

This paper presents a study of the segmentation of medical images.The paper provides a solid introduction to image enhancement along with image segmentation fundamentals.In the first step,the morphological operations are employed to ensure image detail protection and noise-immunity.The objective of using morphological operations is to remove the defects in the texture of the image.Secondly,the Fuzzy C-Means(FCM)clustering algorithm is used to modify membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster centers.The proposed technique is very simple to implement and significantly fast since it is not necessary to compute the distance between the neighboring pixels and the cluster centers.It is also efficient when dealing with noisy images because of its ability to efficiently improve the membership partition matrix.Simulation results are performed on different medical image modalities.Ultrasonic(Us),X-ray(Mammogram),Computed Tomography(CT),Positron Emission Tomography(PET),and Magnetic Resonance(MR)images are the main medical image modalities used in this work.The obtained results illustrate that the proposed technique can achieve good results with a short time and efficient image segmentation.Simulation results on different image modalities show that the proposed technique can achieve segmentation accuracies of 98.83%,99.71%,99.83%,99.85%,and 99.74%for Us,Mammogram,CT,PET,and MRI images,respectively.

关键词： Image segmentation ultrasonic mammogram CT PET MRI morphological operations FCM active contours

In-situ 3D contour measurement for laser powder bed fusion based on phase guidance

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Theoretical & Applied Mechanics Letters 2023年第2期13卷 113-119页

作者： Yuze Zhang Pan Zhang Xin Jiang Siyuan Zhang Kai Zhong Zhongwei Li State Key Lab of Materials Processing and Die&Mould Technology Huazhong University of Science and TechnologyWuhan430074China Research Institute of Huazhong University of Science and Technology in Shenzhen Shenzhen51800China

In-situ layerwise imaging measurement of laser powder bed fusion(LPBF)provides a wealth of forming and defect data which enables monitoring of components quality and powder bed homogeneity.Using high-resolution camera layerwise imaging and image processing algorithms to monitor fusion area and powder bed geometric defects has been studied by many researchers,which successfully monitored the contours of components and evaluated their accuracy.However,research for the methods of in-situ 3D contour measurement or component edge warping identification is rare.In this study,a 3D contour mea-surement method combining gray intensity and phase difference is proposed,and its accuracy is verified by designed experiments.The results show that the high-precision of the 3D contours can be achieved by the constructed energy minimization function.This method can detect the deviations of common ge-ometric features as well as warpage at LPBF component edges,and provides fundamental data for in-situ quality monitoring tools.

关键词： Laser powder bed fusion In-situ measurement active contours 3D contour Measurement accuracy

New normalized nonlocal hybrid level set method for image segmentation

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Applied Mathematics(A Journal of Chinese Universities) 2017年第4期32卷 407-421页

作者： LOU Qiong PENG Jia-lin KONG De-xing WANG Chun-lin Department of Mathematics Zhejiang University of Science and Technology The School of Computer Science and Technology Huaqiao University Department of Mathematics Zhejiang University Department of Pediatrics The First HostipalCollege of MedicineZhejiang University

This article introduces a new normalized nonlocal hybrid level set method for image segmentation.Due to intensity overlapping,blurred edges with complex backgrounds,simple intensity and texture information,such kind of image segmentation is still a challenging task.The proposed method uses both the region and boundary information to achieve accurate segmentation results.The region information can help to identify rough region of interest and prevent the boundary leakage problem.It makes use of normalized nonlocal comparisons between pairs of patches in each region,and a heuristic intensity model is proposed to suppress irrelevant strong edges and constrain the segmentation.The boundary information can help to detect the precise location of the target object,it makes use of the geodesic active contour model to obtain the target boundary.The corresponding variational segmentation problem is implemented by a level set formulation.We use an internal energy term for geometric active contours to penalize the deviation of the level set function from a signed distance function.At last,experimental results on synthetic images and real images are shown in the paper with promising results.

关键词： image segmentation level set method nonlocal method intensity information active contours normalization

Multigrid Method for the Chan-Vese Model in Variational Segmentation

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Communications in Computational Physics 2008年第7期4卷 294-316页

作者： Noor Badshah Ke Chen Department of Mathematical Sciences University of LiverpoolLiverpool L693BXUK.

The Chan-Vese method of active contours without edges[11]has been used successfully for segmentation of images.As a variational formulation,it involves the solution of a fully nonlinear partial differential equation which is usually solved by using time marching methods with semi-implicit schemes for a parabolic equation;the recent method of additive operator splitting[19,36]provides an effective acceleration of such schemes for images of moderate size.However to process images of large size,urgent need exists in developing fast multilevel methods.Here we present a multigrid method to solve the Chan-Vese nonlinear elliptic partial differential equation,and demonstrate the fast convergence.We also analyze the smoothing rates of the associated smoothers.Based on our numerical tests,a surprising observation is that our multigrid method is more likely to converge to the global minimizer of the particular non-convex problem than previously unilevel methods which may get stuck at local minimizers.Numerical examples are given to show the expected gain in CPU time and the added advantage of global solutions.

关键词： active contours energy minimization partial differential equations segmentation level sets total variation multigrid.

Image Selective Segmentation under Geometrical Constraints Using an active Contour Approach

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Communications in Computational Physics 2010年第4期7卷 759-778页

作者： Noor Badshah Ke Chen Centre for Mathematical Imaging Techniques(CMIT) Department of Mathematical SciencesThe University of LiverpoolLiverpool L697ZLUnited Kingdom

In this paper we propose a new model for segmentation of an image under some geometrical constraints in order to detect special regions of interest.Our work is based on the recent work by Gout et al.[Numer.Algorithms,39(2005),pp.155-173 and 48(2008),pp.105-133]using geodesic active contours models,by combining it with the idea of a piecewise constant Mumford-Shah model as with the non-selective Chan-Vese segmentation.Numerical tests show that our method is more robust than the previous works.

关键词： active contours energy minimization partial differential equations segmentation level sets geometric constraints.

Advance Techniques in Medical Imaging under Big Data Analysis: Covid-19 Images

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Advances in Computed Tomography 2021年第1期10卷 1-10页

作者： S. Zimeras Department of Statistics and Actuarial-Financial Mathematics University of the Aegean Karlovassi Samos

Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. Accurate segmentation is required for volume determination, 3D rendering, radiation therapy, and surgery planning. In medical images, segmentation has traditionally been done by human experts. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore, automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. Many methods have been proposed to detect and segment 2D shapes, most of which involve template matching. Advanced segmentation techniques called Snakes or active contours have been used, considering deformable models or templates. The main purpose of this work is to apply segmentation techniques for the definition of 3D organs (anatomical structures) when big data information has been stored and must be organized by the doctors for medical diagnosis. The processes would be implemented in the CT images from patients with COVID-19.

关键词： Segmentation Techniques Big Data Analysis Contour Model Shape Model Radial Basis Function active contours Snakes

AN active CONTOUR BASED ALGORITHM FOR CELL PLANNING

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AN ACTIVE CONTOUR BASED ALGORITHM FOR CELL PLANNING