scholarly journals Magnetic Resonance Imaging Segmentation via Weighted Level Set Model Based on Local Kernel Metric and Spatial Constraint

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1196
Author(s):  
Jianhua Song ◽  
Zhe Zhang
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Level Set ◽  
Resonance Imaging ◽  
Spatial Constraint ◽  
Low Contrast ◽  
Level Set Function ◽  
Set Function ◽  
Distance Regularization ◽  
Level Set Model

Magnetic resonance imaging (MRI) segmentation is a fundamental and significant task since it can guide subsequent clinic diagnosis and treatment. However, images are often corrupted by defects such as low-contrast, noise, intensity inhomogeneity, and so on. Therefore, a weighted level set model (WLSM) is proposed in this study to segment inhomogeneous intensity MRI destroyed by noise and weak boundaries. First, in order to segment the intertwined regions of brain tissue accurately, a weighted neighborhood information measure scheme based on local multi information and kernel function is designed. Then, the membership function of fuzzy c-means clustering is used as the spatial constraint of level set model to overcome the sensitivity of level set to initialization, and the evolution of level set function can be adaptively changed according to different tissue information. Finally, the distance regularization term in level set function is replaced by a double potential function to ensure the stability of the energy function in the evolution process. Both real and synthetic MRI images can show the effectiveness and performance of WLSM. In addition, compared with several state-of-the-art models, segmentation accuracy and Jaccard similarity coefficient obtained by WLSM are increased by 0.0586, 0.0362 and 0.1087, 0.0703, respectively.

scholarly journals An Image Segmentation Method Based on Improved Regularized Level Set Model

2018 ◽  
Vol 8 (12) ◽  
pp. 2393 ◽  
Author(s):  
Lin Sun ◽  
Xinchao Meng ◽  
Jiucheng Xu ◽  
Shiguang Zhang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Energy Functional ◽  
External Energy ◽  
Regularization Term ◽  
Level Set Function ◽  
New Energy ◽  
Segmentation Approach ◽  
Set Function ◽  
Distance Regularization

When the level set algorithm is used to segment an image, the level set function must be initialized periodically to ensure that it remains a signed distance function (SDF). To avoid this defect, an improved regularized level set method-based image segmentation approach is presented. First, a new potential function is defined and introduced to reconstruct a new distance regularization term to solve this issue of periodically initializing the level set function. Second, by combining the distance regularization term with the internal and external energy terms, a new energy functional is developed. Then, the process of the new energy functional evolution is derived by using the calculus of variations and the steepest descent approach, and a partial differential equation is designed. Finally, an improved regularized level set-based image segmentation (IRLS-IS) method is proposed. Numerical experimental results demonstrate that the IRLS-IS method is not only effective and robust to segment noise and intensity-inhomogeneous images but can also analyze complex medical images well.

Parametric Shape and Topology Optimization: A New Level Set Approach Based on Cardinal Kernel Functions

2017 ◽  
Author(s):  
Long Jiang ◽  
Shikui Chen ◽  
Xiangmin Jiao
Keyword(s):  
Topology Optimization ◽  
Level Set Method ◽  
Level Set ◽  
Material Property ◽  
Kernel Function ◽  
Level Set Methods ◽  
Level Set Function ◽  
Set Function ◽  
Distance Regularization ◽  
Parametric Level Set Method

The parametric level set method is an extension of the conventional level set methods for topology optimization. By parameterizing the level set function, conventional levels let methods can be easily coupled with mathematical programming to achieve better numerical robustness and computational efficiency. Furthermore, the parametric level set scheme not only can inherit the original advantages of the conventional level set methods, such as clear boundary representation and high topological changes handling flexibility but also can alleviate some un-preferred features from the conventional level set methods, such as needing re-initialization. However, in the RBF-based parametric level set method, it was difficult to determine the range of the design variables. Moreover, with the mathematically driven optimization process, the level set function often results in significant fluctuations during the optimization process. This brings difficulties in both numerical stability control and material property interpolation. In this paper, an RBF partition of unity collocation method is implemented to create a new type of kernel function termed as the Cardinal Basis Function (CBF), which employed as the kernel function to parameterize the level set function. The advantage of using the CBF is that the range of the design variable, which was the weight factor in conventional RBF, can be explicitly specified. Additionally, a distance regularization energy functional is introduced to maintain a desired distance regularized level set function evolution. With this desired distance regularization feature, the level set evolution is stabilized against significant fluctuations. Besides, the material property interpolation from the level set function to the finite element model can be more accurate.

A VARIATIONAL MULTIPHASE LEVEL SET ALGORITHM FOR IMAGE SEGMENTATION BASED ON ACTIVE CONTOUR MODEL

2013 ◽  
Vol 09 (01) ◽  
pp. 1250004 ◽  
Author(s):  
HAIYING LIU ◽  
YU CHENG ◽  
MAX Q.-H. MENG
Keyword(s):  
Mathematical Model ◽  
Image Segmentation ◽  
Level Set ◽  
Active Contour Model ◽  
Previous Method ◽  
Regularization Term ◽  
Level Set Function ◽  
Set Function ◽  
Distance Regularization ◽  
Double Well Potential

A novel variational multiphase level set mathematical model is derived for image segmentation with two contributions. By virtue of eliminating the time-consuming re-initialization procedure and neglecting the property of the level set function during the evolution process, we in this paper present two strategies that may be taken as our contributions to solving these problems. Two scenarios are considered, namely, first, the distance regularization term which is defined by double-well potential function with two minimum points is introduced to our mathematical model for avoiding the re-initialization process. Second, by combining a Tikhonov-like regularization term which can guarantee the smoothness for the evolution curve over the previous method. Numerical simulation studies are presented to verify our new model via evaluating and comparing with existing algorithms.

scholarly journals Segmentation of Low Contrast Satellite and Medical Images Based on Level Set Function with Harmony Search Optimization Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 7306-7313
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Medical Images ◽  
Search Algorithm ◽  
Harmony Search ◽  
Threshold Level ◽  
Harmony Search Algorithm ◽  
Low Contrast ◽  
Level Set Function ◽  
Set Function

The main aim of digital image segmentation for portioned the image in to its constituents parts for getting information regarding features of image also used to get pathological details from medical images. The literature available from last two decades the important scheme for image segmentation is with Level Set technique, multilevel thresholding of gray scale on histogram of image is also a traditional method of image segmentation. In this paper low contrast images from medical and satellite images considered for image segmentation to extract features. This paper puts forward a novel image segmentation method via Level Set Function along with BiHistogram Equalization based on Harmony Search Algorithm(LSFBHEHS). The Selective Binary and Gaussian Filtering Regularised Level Set (SBGFRLS) is efficient novel region based Active Contour Model, it uses a novel region-based signed pressure force (SPF) function, it can adeptly halt the contours at blurred edges and weak edges. Other important advantage is internal and external boundaries can be distinguished by fixing the initial contour may be anyplace in the considered image. This method is resourceful but requires more time and inefficient for segmentation of low contrast images. This problem is rectified by applying bi-histogram equalization(BHE) image enhancement method prior to Level Set, it can be treated as pre-processing. In BHE technique of image enhancement, the image histogram is partitioned into two divisions based optimized gray level threshold , and equalize each part of histogram separately and combined later. To find the optimized threshold level to slice the histogram into two parts, Otsu’s multilevel thresholding method used to find threshold level, to find optimized thresholding level Harmony Search Algorithm(HSA) is implemented to maximize inert class variance as objective function. For evaluating the proposed method and SBGFRLS, the qualitative measured used like Dice similarity index, Measure of Enhancement(EME) and time required, for experimentation numerous low contrast satellite and medical images are considered, results clarified that the proposed method is more efficient for low contrast and inhomogeneous images.

Applications of Iron Oxide Nanoparticles in Magnetic Resonance Imaging (MRI)

2020 ◽  
Vol 10 ◽  
Author(s):  
Pratishtha Kushwaha ◽  
Pratima Chauhan
Keyword(s):  
Magnetic Resonance Imaging ◽  
Iron Oxide ◽  
Magnetic Resonance ◽  
Iron Oxide Nanoparticles ◽  
The Body ◽  
Oxide Nanoparticles ◽  
Resonance Imaging ◽  
Low Contrast ◽  
Magnetic Resonance Imaging Mri ◽  
Surface Covering

Abstract: Iron oxide nanoparticles by the help of legitimate surface science have been broadly utilized tentatively in many living organism applications, for example magnetic resonance imaging (MRI), drug delivery, Repair of tissue, immunobiology, hyperthermia, detoxification of natural liquids, differentiate improvement between low contrast and high contrast tissues, and in cell partition and so on. What's more, these applications need exceptional nontoxic and biocompatible surface covering of the attractive particles to permit a targetable conveyance with molecular restriction in a particular territory. The general size of the colloid can be estimated ordinarily of surface covering and their resulting spatial course of action adjacent to the nanoparticles, it additionally assumes a critical job in bio-kinetics and bio-distribution of nanoparticles in the body. The kinds of surface coatings, for this sort of nanoparticles rely upon the kind of application and ought to be picked by remembering a specific application, regardless of whether it is gone for aggravation reaction or anti-cancer agents. In this paper, we examine the manufactured science, liquid adjustment and surface change of iron oxide nanoparticles, just as their utilization for biomedical applications

scholarly journals Application of the Level-Set Model with Constraints in Image Segmentation

2016 ◽  
Vol 9 (1) ◽  
pp. 147-168
Author(s):  
Vladimír Klement ◽  
Tomáš Oberhuber ◽  
Daniel Ševčovič
Keyword(s):  
Image Segmentation ◽  
Level Set Method ◽  
Level Set ◽  
A Priori ◽  
Linear Complementarity Problems ◽  
Relaxation Method ◽  
Level Set Function ◽  
Priori Information ◽  
Set Function ◽  
Level Set Model

AbstractWe propose and analyze a constrained level-set method for semi-automatic image segmentation. Our level-set model with constraints on the level-set function enables us to specify which parts of the image lie inside respectively outside the segmented objects. Such a-priori information can be expressed in terms of upper and lower constraints prescribed for the level-set function. Constraints have the same conceptual meaning as initial seeds of the popular graph-cuts based methods for image segmentation. A numerical approximation scheme is based on the complementary-finite volumes method combined with the Projected successive over-relaxation method adopted for solving constrained linear complementarity problems. The advantage of the constrained level-set method is demonstrated on several artificial images as well as on cardiac MRI data.

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