scholarly journals Active Contours Using Additive Local and Global Intensity Fitting Models for Intensity Inhomogeneous Image Segmentation

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Shafiullah Soomro ◽  
Farhan Akram ◽  
Jeong Heon Kim ◽  
Toufique Ahmed Soomro ◽  
Kwang Nam Choi
Keyword(s):  
Level Set ◽  
Active Contours ◽  
Energy Functional ◽  
Contour Method ◽  
Intensity Information ◽  
Additive Formulation ◽  
Active Contour Method ◽  
Local Intensity ◽  
Cv Model ◽  
Level Set Formulation

This paper introduces an improved region based active contour method with a level set formulation. The proposed energy functional integrates both local and global intensity fitting terms in an additive formulation. Local intensity fitting term influences local force to pull the contour and confine it to object boundaries. In turn, the global intensity fitting term drives the movement of contour at a distance from the object boundaries. The global intensity term is based on the global division algorithm, which can better capture intensity information of an image than Chan-Vese (CV) model. Both local and global terms are mutually assimilated to construct an energy function based on a level set formulation to segment images with intensity inhomogeneity. Experimental results show that the proposed method performs better both qualitatively and quantitatively compared to other state-of-the-art-methods.

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 Active Contours Using Harmonic Global Division Function

2019 ◽  
Vol 9 (4) ◽  
pp. 4457-4462
Author(s):  
M. T. Bhatti ◽  
S. Soomro ◽  
A. M. Bughio ◽  
T. A. Soomro ◽  
A. Anwar ◽  
...  
Keyword(s):  
Level Set ◽  
Active Contours ◽  
State Of The Art ◽  
Gaussian Kernel ◽  
Pressure Force ◽  
Harmonic Intensity ◽  
Low Contrast ◽  
Intensity Information ◽  
New Energy ◽  
Previous State

This paper presents the region-based active contours method based on the harmonic global signed pressure force (HGSPF) function. The proposed formulation improves the performance of the level set method by utilizing intensity information based on the global division function, which has the ability to segment out regions with higher intensity differences. The new energy utilizes harmonic intensity, which can better preserve the low contrast details and can segment complicated areas easily. A Gaussian kernel is adjusted to regularize level set and to escape an expensive reinitialization. Finally, a set of real and synthetic images are used for validation of the proposed method. Results demonstrate the performance of the proposed method, the accuracy values are compared to previous state-of-the-art methods.

ACTIVE CONTOURS DRIVEN BY LOCAL INTENSITY AND LOCAL GRADIENT FITTING ENERGIES

2014 ◽  
Vol 28 (03) ◽  
pp. 1455006 ◽  
Author(s):  
JIANJUN YUAN ◽  
JIANJUN WANG ◽  
LIPEI LIU
Keyword(s):  
Level Set ◽  
Active Contours ◽  
Active Contour Model ◽  
Signal To Noise Ratio ◽  
Local Region ◽  
Gradient Term ◽  
Ultrasound Images ◽  
Local Intensity ◽  
Level Set Evolution ◽  
Local Gradient

This paper presents a new local region-based and local gradient-based active contour model in a variational level set formulation for image segmentation. The model consists of three parts: the local region term, the local gradient term and the regularization term. The local region term is insensitive to noise, while the local gradient term has better capability of localization than the local region. The energy minimization is achieved by the level set evolution in an iterative strategy. In each iteration, the local intensity and local gradient are updated and fed into the level set evolution. Comparative experiments show that our model achieves the better performance on the ultrasound images with noise and low signal-to-noise ratio than the local binary fitting (LBF) energy model.

scholarly journals Segmentation of Intensity Inhomogeneous Brain MR Images Using Active Contours

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Farhan Akram ◽  
Jeong Heon Kim ◽  
Han Ul Lim ◽  
Kwang Nam Choi
Keyword(s):  
Time Complexity ◽  
Active Contour ◽  
Active Contours ◽  
Contour Method ◽  
Gaussian Kernel ◽  
Mean Values ◽  
Gradient Information ◽  
Level Set Function ◽  
Active Contour Method ◽  
Set Function

Segmentation of intensity inhomogeneous regions is a well-known problem in image analysis applications. This paper presents a region-based active contour method for image segmentation, which properly works in the context of intensity inhomogeneity problem. The proposed region-based active contour method embeds both region and gradient information unlike traditional methods. It contains mainly two terms, area and length, in which the area term practices a new region-based signed pressure force (SPF) function, which utilizes mean values from a certain neighborhood using the local binary fitted (LBF) energy model. In turn, the length term uses gradient information. The novelty of our method is to locally compute new SPF function, which uses local mean values and is able to detect boundaries of the homogenous regions. Finally, a truncated Gaussian kernel is used to regularize the level set function, which not only regularizes it but also removes the need of computationally expensive reinitialization. The proposed method targets the segmentation problem of intensity inhomogeneous images and reduces the time complexity among locally computed active contour methods. The experimental results show that the proposed method yields better segmentation result as well as less time complexity compared with the state-of-the-art active contour methods.

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.

scholarly journals Kidney Segmentation in CT Data Using Hybrid Level-Set Method with Ellipsoidal Shape Constraints

2017 ◽  
Vol 24 (1) ◽  
pp. 101-112 ◽  
Author(s):  
Andrzej Skalski ◽  
Katarzyna Heryan ◽  
Jacek Jakubowski ◽  
Tomasz Drewniak
Keyword(s):  
Computed Tomography ◽  
Level Set ◽  
Renal Cancer ◽  
Imaging Techniques ◽  
Three Dimensional ◽  
Spatial Arrangement ◽  
Energy Functional ◽  
Contour Method ◽  
Ellipsoidal Shape ◽  
Kidney Segmentation

Abstract With development of medical diagnostic and imaging techniques the sparing surgeries are facilitated. Renal cancer is one of examples. In order to minimize the amount of healthy kidney removed during the treatment procedure, it is essential to design a system that provides three-dimensional visualization prior to the surgery. The information about location of crucial structures (e.g. kidney, renal ureter and arteries) and their mutual spatial arrangement should be delivered to the operator. The introduction of such a system meets both the requirements and expectations of oncological surgeons. In this paper, we present one of the most important steps towards building such a system: a new approach to kidney segmentation from Computed Tomography data. The segmentation is based on the Active Contour Method using the Level Set (LS) framework. During the segmentation process the energy functional describing an image is the subject to minimize. The functional proposed in this paper consists of four terms. In contrast to the original approach containing solely the region and boundary terms, the ellipsoidal shape constraint was also introduced. This additional limitation imposed on evolution of the function prevents from leakage to undesired regions. The proposed methodology was tested on 10 Computed Tomography scans from patients diagnosed with renal cancer. The database contained the results of studies performed in several medical centers and on different devices. The average effectiveness of the proposed solution regarding the Dice Coefficient and average Hausdorff distance was equal to 0.862 and 2.37 mm, respectively. Both the qualitative and quantitative evaluations confirm effectiveness of the proposed solution.

Sign in / Sign up

Export Citation Format

Share Document