scholarly journals On the Relationship between Variational Level Set-Based and SOM-Based Active Contours

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Mohammed M. Abdelsamea ◽  
Giorgio Gnecco ◽  
Mohamed Medhat Gaber ◽  
Eyad Elyan
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contours ◽  
Level Set Methods ◽  
Energy Functional ◽  
Self Organizing Maps ◽  
Complex Shapes ◽  
Variational Level Set ◽  
Preservation Property ◽  
The Relationship

Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods have been used to build an active contour with the aim of modeling arbitrarily complex shapes. Moreover, they can handle also topological changes of the contours. Self-Organizing Maps (SOMs) have attracted the attention of many computer vision scientists, particularly in modeling an active contour based on the idea of utilizing the prototypes (weights) of a SOM to control the evolution of the contour. SOM-based models have been proposed in general with the aim of exploiting the specific ability of SOMs to learn the edge-map information via their topology preservation property and overcoming some drawbacks of other ACMs, such as trapping into local minima of the image energy functional to be minimized in such models. In this survey, we illustrate the main concepts of variational level set-based ACMs, SOM-based ACMs, and their relationship and review in a comprehensive fashion the development of their state-of-the-art models from a machine learning perspective, with a focus on their strengths and weaknesses.

scholarly journals Medical Image Segmentation Based on a Hybrid Region-Based Active Contour Model

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Tingting Liu ◽  
Haiyong Xu ◽  
Wei Jin ◽  
Zhen Liu ◽  
Yiming Zhao ◽  
...  
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contours ◽  
Active Contour Model ◽  
Energy Functional ◽  
Medical Image Segmentation ◽  
Regularization Term ◽  
Contour Model ◽  
Proposed Model ◽  
Hybrid Region

A novel hybrid region-based active contour model is presented to segment medical images with intensity inhomogeneity. The energy functional for the proposed model consists of three weighted terms: global term, local term, and regularization term. The total energy is incorporated into a level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Experiments on some synthetic and real images demonstrate that our model is more efficient compared with the localizing region-based active contours (LRBAC) method, proposed by Lankton, and more robust compared with the Chan-Vese (C-V) active contour model.

scholarly journals New Region-Scalable Discriminant and Fitting Energy Functional for Driving Geometric Active Contours in Medical Image Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Xuchu Wang ◽  
Yanmin Niu ◽  
Liwen Tan ◽  
Shao-Xiang Zhang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Active Contour ◽  
Medical Image ◽  
Active Contours ◽  
Active Contour Model ◽  
Energy Functional ◽  
Medical Image Segmentation ◽  
Intensity Inhomogeneity ◽  
Boundary Problems

We propose a novel region-based geometric active contour model that uses region-scalable discriminant and fitting energy functional for handling the intensity inhomogeneity and weak boundary problems in medical image segmentation. The region-scalable discriminant and fitting energy functional is defined to capture the image intensity characteristics in local and global regions for driving the evolution of active contour. The discriminant term in the model aims at separating background and foreground in scalable regions while the fitting term tends to fit the intensity in these regions. This model is then transformed into a variational level set formulation with a level set regularization term for accurate computation. The new model utilizes intensity information in the local and global regions as much as possible; so it not only handles better intensity inhomogeneity, but also allows more robustness to noise and more flexible initialization in comparison to the original global region and regional-scalable based models. Experimental results for synthetic and real medical image segmentation show the advantages of the proposed method in terms of accuracy and robustness.

IMAGE COSEGMENTATION BASED ON LOCAL AND GLOBAL LEVEL SET METHODS

2012 ◽  
Vol 12 (03) ◽  
pp. 1250019 ◽  
Author(s):  
LIHE ZHANG ◽  
ZHENZHEN LIU
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Level Set Methods ◽  
Energy Functional ◽  
Image Statistics ◽  
Contour Model ◽  
Contour Models ◽  
Homogeneous Regions ◽  
The Given ◽  
Edge Indicator Function

In this paper, we propose a novel cosegmentation algorithm based on active contour model which utilizes local and global image statistics. Many localized region-based active contour models have been proposed to solve a challenging problem of the property (such as intensity, color, texture, etc.) inhomogeneities that often occurs in real images, but these models usually cannot reasonably evolve the curve in this situation that some center points along the curve are in homogeneous regions and their local regions are far away from the object. In order to overcome the difficulties we selectively enlarge the driven force of some points and introduce the edge indicator function to avoid the curve over-shrinking or over-expanding on the salient boundaries. In addition, we introduce global image statistics to better the curve evolution and try to avoid the given energy functional converging to a local minimum. Practical experiments show that our algorithm can obtain better segmentation results.

scholarly journals Implicit Active Contours Driven by Local and Global Image Fitting Energy for Image Segmentation and Target Localization

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaosheng Yu ◽  
Yuanchen Qi ◽  
Ziwei Lu ◽  
Nan Hu
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contours ◽  
Active Contour Model ◽  
Target Localization ◽  
Numerical Accuracy ◽  
Variational Level Set ◽  
Contour Evolution ◽  
Level Set Formulation ◽  
Local Image

We propose a novel active contour model in a variational level set formulation for image segmentation and target localization. We combine a local image fitting term and a global image fitting term to drive the contour evolution. Our model can efficiently segment the images with intensity inhomogeneity with the contour starting anywhere in the image. In its numerical implementation, an efficient numerical schema is used to ensure sufficient numerical accuracy. We validated its effectiveness in numerous synthetic images and real images, and the promising experimental results show its advantages in terms of accuracy, efficiency, and robustness.

scholarly journals Comparison of Three Segmentation Methods for Breast Ultrasound Images based on Level Set and Morphological Operations

2017 ◽  
Vol 7 (1) ◽  
pp. 383
Author(s):  
Dewi Putrie Lestari ◽  
Sarifuddin Madenda ◽  
Ernastuti Ernastuti ◽  
Eri Prasetyo Wibowo
Keyword(s):  
Breast Cancer ◽  
Level Set ◽  
Active Contour ◽  
Level Set Methods ◽  
Breast Ultrasound ◽  
Medical Image Segmentation ◽  
Image Processing Technique ◽  
Dice Similarity Coefficient ◽  
Ultrasound Images ◽  
Morphological Operations

Breast cancer is one of the major causes of death among women all over the world. The most frequently used diagnosis tool to detect breast cancer is ultrasound. However, to segment the breast ultrasound images is a difficult thing. Some studies show that the active contour models have been proved to be the most successful methods for medical image segmentation. The level set method is a class of curve evolution methods based on the geometric active contour model. Morphological operation describes a range of image processing technique that deal with the shape of features in an image. Morphological operations are applied to remove imperfections that introduced during segmentation. In this paper, we have evaluated three level set methods that combined with morphological operations to segment the breast lesions. The level set methods that used in our research are the Chan Vese (C-V) model, the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model and the Distance Regularized Level Set Evolution (DRLSE) model. Furthermore, to evaluate the method, we compared the segmented breast lesion that obtained by each method with the lesion that obtained manually by radiologists. The evaluation is done by four metrics: Dice Similarity Coefficient (DSC), True-Positive Ratio (TPR), True-Negative Ratio (TNR), and Accuracy (ACC). Our experimental results with 30 breast ultrasound images showed that the C-V model that combined with morphological operations have better performance than the other two methods according to mean value of DSC metrics.

scholarly journals Level Set Segmentation: Active Contours without edge

2009 ◽  
Author(s):  
Kishore Mosaliganti ◽  
Benjamin Smith ◽  
Arnaud Gelas ◽  
Alexandre Gouaillard ◽  
sean megason
Keyword(s):  
Level Set ◽  
Level Sets ◽  
Active Contours ◽  
Level Set Methods ◽  
Constant Intensity ◽  
Piecewise Constant ◽  
Gradient Information ◽  
Cardiac Image ◽  
Contour Evolution ◽  
Processing Framework

An Insight Toolkit (ITK) processing framework for segmentation using active contours without edges is presented in this paper. Our algorithm is based on the work of Chan and Vese [1] that uses level- sets to accomplish region segmentation in images with poor or no gradient information. The basic idea is to partion the image into two piecewise constant intensity regions. This work is in contrast to the level-set methods currently available in ITK which necessarily require gradient information. Similar to those methods, the methods presented in this paper are also made efficient using a sparse implementation strategy that solves the contour evolution PDE at the level-set boundary. The framework consists of 6 new ITK filters that inherit in succession from itk::SegmentationFilter. We include 2D/3D example code, parameter settings and show the results generated on a 2D cardiac image.

scholarly journals level set segmentation using coupled active surfaces

2009 ◽  
Author(s):  
Kishore Mosaliganti ◽  
Benjamin Smith ◽  
Arnaud Gelas ◽  
Alexandre Gouaillard ◽  
sean megason
Keyword(s):  
Level Set ◽  
Level Sets ◽  
Active Contours ◽  
Level Set Methods ◽  
Multiple Objects ◽  
Constant Intensity ◽  
Piecewise Constant ◽  
Gradient Information ◽  
Cardiac Image ◽  
Contour Evolution

An Insight Toolkit (ITK) processing framework for simultaneous segmentation of multiple objects using active contours without edges is presented in this paper. These techniques are also popularly referred to as multiphase methods. Earlier, we had an implemented the Chan and Vese [1] algorithm that uses level- sets to accomplish region segmentation in images with poor or no gradient information. The current work extends that submission to use multiple level sets that evolve concurrently. The basic idea is to partion the image into several sets of piecewise constant intensity regions. This work is in contrast to the level-set methods currently available in ITK which necessarily require gradient information and also necessarily segment a single object-of-interest. Similar to those methods, the methods presented in this paper are also made efficient using a sparse implementation strategy that solves the contour evolution PDE at the level-set boundary. This work does not introduce any new filter but extends the earlier submitted to filters to process multiple objects. We include 2D/3D example code, parameter settings and show the results generated on a 2D cardiac image.

scholarly journals Convex Image Segmentation Model Based on Local and Global Intensity Fitting Energy and Split Bregman Method

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Yunyun Yang ◽  
Boying Wu
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Energy Functional ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Proposed Model ◽  
Variational Level Set ◽  
Convex Image Segmentation ◽  
Level Set Formulation ◽  
Global Fitting

We propose a convex image segmentation model in a variational level set formulation. Both the local information and the global information are taken into consideration to get better segmentation results. We first propose a globally convex energy functional to combine the local and global intensity fitting terms. The proposed energy functional is then modified by adding an edge detector to force the active contour to the boundary more easily. We then apply the split Bregman method to minimize the proposed energy functional efficiently. By using a weight function that varies with location of the image, the proposed model can balance the weights between the local and global fitting terms dynamically. We have applied the proposed model to synthetic and real images with desirable results. Comparison with other models also demonstrates the accuracy and superiority of the proposed model.

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