scholarly journals A Novel Fuzzy Level Set Approach for Image Contour Detection

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Yingjie Zhang ◽  
Jianxing Xu ◽  
H. D. Cheng
Keyword(s):  
Differential Equation ◽  
Level Set ◽  
Energy Function ◽  
Level Set Methods ◽  
Contour Detection ◽  
Low Contrast ◽  
Probability Densities ◽  
Entropy Principle ◽  
Image Contour ◽  
Level Set Approach

The level set methods have provided powerful frameworks for image segmentation. However, to obtain accurate boundaries of the objects, especially when they have weak edges or inhomogeneous intensities, is still a very challenging task. Actually, we have studied the popular existing level set approaches and discovered that they failed to segment the images with weak edges or inhomogeneous intensities in many cases. The weak/blurry edges and inhomogeneous intensities cause uncertainty and fuzziness for segmentation. In this paper, a novel fuzzy level set approach is proposed. At first,S-function based on the maximum fuzzy entropy principle (MEP) is used to map the image from space domain to fuzzy domain. Then, an energy function is formulated according to the differences between the actual and estimated probability densities of the intensities in different regions. A partial differential equation is derived for finding the minimum of the energy function. The proposed approach has been tested on both synthetic images and real images and evaluated by several popular metrics. The experimental results demonstrate that the proposed approach can locate the true object boundaries, even for objects with blurry boundaries, low contrast, and inhomogeneous intensities.

scholarly journals A perturbation view of level-set methods for convex optimization

2020 ◽  
Vol 14 (8) ◽  
pp. 1989-2006
Author(s):  
Ron Estrin ◽  
Michael P. Friedlander
Keyword(s):  
Convex Optimization ◽  
Level Set ◽  
Level Set Methods ◽  
Sufficient Conditions ◽  
Strong Duality ◽  
Necessary Condition ◽  
Root Finding ◽  
Convex Problems ◽  
Level Set Approach ◽  
Slater's Constraint Qualification

Abstract Level-set methods for convex optimization are predicated on the idea that certain problems can be parameterized so that their solutions can be recovered as the limiting process of a root-finding procedure. This idea emerges time and again across a range of algorithms for convex problems. Here we demonstrate that strong duality is a necessary condition for the level-set approach to succeed. In the absence of strong duality, the level-set method identifies $$\epsilon $$ ϵ -infeasible points that do not converge to a feasible point as $$\epsilon $$ ϵ tends to zero. The level-set approach is also used as a proof technique for establishing sufficient conditions for strong duality that are different from Slater’s constraint qualification.

scholarly journals Contour detection in an infrared image using the modified snake algorithm

2018 ◽  
Vol 226 ◽  
pp. 04049
Author(s):  
Viacheslav V. Voronin ◽  
Oxana S. Balabaeva ◽  
Marina M. Pismenskova ◽  
Svetlana V. Tokareva ◽  
Irina V. Tolstova
Keyword(s):  
Active Contours ◽  
Infrared Image ◽  
Detection Algorithm ◽  
Contour Detection ◽  
Modified Model ◽  
Thermal Images ◽  
Low Contrast ◽  
Security Applications ◽  
Image Contour ◽  
Snake Algorithm

Infrared and thermal images have been used widely in the different forensics and security applications. Such images show the temperature difference between different objects and scene background. One of the drawbacks of such images is low contrast and noisy images which should be enhanced. This paper presents a new thermal image contour detection algorithm using the modified snake algorithm. The segmentation algorithm based on the image enhancement and the modified model of active contours based on regions, taking into account the calculation of the anisotropic gradient. Some presented experimental results illustrate the performance of the proposed cloud system on real thermal images in comparison with the traditional methods.

scholarly journals Retina Image Vessel Segmentation Using a Hybrid CGLI Level Set Method

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Guannan Chen ◽  
Meizhu Chen ◽  
Jichun Li ◽  
Encai Zhang
Keyword(s):  
Level Set ◽  
Active Contour Model ◽  
Level Set Methods ◽  
Extraction Methods ◽  
Low Contrast ◽  
Average Accuracy ◽  
Structured Analysis ◽  
Local Intensity ◽  
Vessel Extraction ◽  
Public Datasets

As a nonintrusive method, the retina imaging provides us with a better way for the diagnosis of ophthalmologic diseases. Extracting the vessel profile automatically from the retina image is an important step in analyzing retina images. A novel hybrid active contour model is proposed to segment the fundus image automatically in this paper. It combines the signed pressure force function introduced by the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model with the local intensity property introduced by the Local Binary fitting (LBF) model to overcome the difficulty of the low contrast in segmentation process. It is more robust to the initial condition than the traditional methods and is easily implemented compared to the supervised vessel extraction methods. Proposed segmentation method was evaluated on two public datasets, DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (Structured Analysis of the Retina) (the average accuracy of 0.9390 with 0.7358 sensitivity and 0.9680 specificity on DRIVE datasets and average accuracy of 0.9409 with 0.7449 sensitivity and 0.9690 specificity on STARE datasets). The experimental results show that our method is effective and our method is also robust to some kinds of pathology images compared with the traditional level set methods.

scholarly journals A Segmentation of Melanocytic Skin Lesions in Dermoscopic and Standard Images Using a Hybrid Two-Stage Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Yoo Na Hwang ◽  
Min Ji Seo ◽  
Sung Min Kim
Keyword(s):  
Level Set ◽  
Ground Truth ◽  
Skin Lesions ◽  
Low Contrast ◽  
Biomedical Image ◽  
Clinical Applicability ◽  
Model Combining ◽  
Proposed Model ◽  
Level Set Approach ◽  
Surrounding Skin

The segmentation of a skin lesion is regarded as very challenging because of the low contrast between the lesion and the surrounding skin, the existence of various artifacts, and different imaging acquisition conditions. The purpose of this study is to segment melanocytic skin lesions in dermoscopic and standard images by using a hybrid model combining a new hierarchical K -means and level set approach, called HK-LS. Although the level set method is usually sensitive to initial estimation, it is widely used in biomedical image segmentation because it can segment more complex images and does not require a large number of manually labelled images. The preprocessing step is used for the proposed model to be less sensitive to intensity inhomogeneity. The proposed method was evaluated on medical skin images from two publicly available datasets including the PH2 database and the Dermofit database. All skin lesions were segmented with high accuracies (>94%) and Dice coefficients (>0.91) of the ground truth on two databases. The quantitative experimental results reveal that the proposed method yielded significantly better results compared to other traditional level set models and has a certain advantage over the segmentation results of U-net in standard images. The proposed method had high clinical applicability for the segmentation of melanocytic skin lesions in dermoscopic and standard images.

scholarly journals A Variational Level Set Approach Based on Local Entropy for Image Segmentation and Bias Field Correction

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Jian Tang ◽  
Xiaoliang Jiang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Energy Function ◽  
Bias Field ◽  
Superior Performance ◽  
Local Entropy ◽  
Distribution Fitting ◽  
Field Estimation ◽  
Variational Level Set ◽  
Level Set Approach

Image segmentation has always been a considerable challenge in image analysis and understanding due to the intensity inhomogeneity, which is also commonly known as bias field. In this paper, we present a novel region-based approach based on local entropy for segmenting images and estimating the bias field simultaneously. Firstly, a local Gaussian distribution fitting (LGDF) energy function is defined as a weighted energy integral, where the weight is local entropy derived from a grey level distribution of local image. The means of this objective function have a multiplicative factor that estimates the bias field in the transformed domain. Then, the bias field prior is fully used. Therefore, our model can estimate the bias field more accurately. Finally, minimization of this energy function with a level set regularization term, image segmentation, and bias field estimation can be achieved. Experiments on images of various modalities demonstrated the superior performance of the proposed method when compared with other state-of-the-art approaches.

Image contour detection based on improved level set in complex environment

2021 ◽  
Author(s):  
Dan Li ◽  
Lulu Bei ◽  
Jinan Bao ◽  
Sizhen Yuan ◽  
Kai Huang
Keyword(s):  
Level Set ◽  
Contour Detection ◽  
Complex Environment ◽  
Image Contour

Differential equation based constrained reinitialization for level set methods

2008 ◽  
Vol 227 (14) ◽  
pp. 6821-6845 ◽  
Author(s):  
Daniel Hartmann ◽  
Matthias Meinke ◽  
Wolfgang Schröder
Keyword(s):  
Differential Equation ◽  
Level Set ◽  
Level Set Methods
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