scholarly journals A New Variational Model for Segmenting Objects of Interest from Color Images

2010 ◽  
Vol 2010 ◽  
pp. 1-15 ◽  
Author(s):  
Yanli Zhai ◽  
Boying Wu ◽  
Dazhi Zhang ◽  
Jiebao Sun
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Energy Functional ◽  
Color Images ◽  
Variational Model ◽  
Geodesic Active Contour ◽  
Active Contour Models ◽  
New Model ◽  
New Energy ◽  
Contour Models

We propose a new variational model for segmenting objects of interest from color images. This model is inspired by the geodesic active contour model, the region-scalable fitting model, the weighted bounded variation model and the active contour models based on the Mumford-Shah model. In order to segment desired objects in color images, the energy functional in our model includes a discrimination function that determines whether an image pixel belongs to the desired objects or not. Compared with other active contour models, our new model cannot only avoid the usual drawback in the level set approach but also detect the objects of interest accurately. Moreover, we investigate the new model mathematically and establish the existence of the minimum to the new energy functional. Finally, numerical results show the effectiveness of our proposed model.

scholarly journals Active Contour Model Using Fast Fourier Transformation for Salient Object Detection

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 192
Author(s):  
Umer Sadiq Khan ◽  
Xingjun Zhang ◽  
Yuanqi Su
Keyword(s):  
Object Detection ◽  
Fourier Transformation ◽  
Active Contour ◽  
Active Contour Model ◽  
Salient Object Detection ◽  
Fast Fourier Transformation ◽  
Salient Object ◽  
Active Contour Models ◽  
Contour Model ◽  
Contour Models

The active contour model is a comprehensive research technique used for salient object detection. Most active contour models of saliency detection are developed in the context of natural scenes, and their role with synthetic and medical images is not well investigated. Existing active contour models perform efficiently in many complexities but facing challenges on synthetic and medical images due to the limited time like, precise automatic fitted contour and expensive initialization computational cost. Our intention is detecting automatic boundary of the object without re-initialization which further in evolution drive to extract salient object. For this, we propose a simple novel derivative of a numerical solution scheme, using fast Fourier transformation (FFT) in active contour (Snake) differential equations that has two major enhancements, namely it completely avoids the approximation of expansive spatial derivatives finite differences, and the regularization scheme can be generally extended more. Second, FFT is significantly faster compared to the traditional solution in spatial domain. Finally, this model practiced Fourier-force function to fit curves naturally and extract salient objects from the background. Compared with the state-of-the-art methods, the proposed method achieves at least a 3% increase of accuracy on three diverse set of images. Moreover, it runs very fast, and the average running time of the proposed methods is about one twelfth of the baseline.

SEGMENTATION OF ABDOMEN DISEASES USING ACTIVE CONTOUR MODELS IN CT IMAGES

2015 ◽  
Vol 27 (05) ◽  
pp. 1550047 ◽  
Author(s):  
Gaurav Sethi ◽  
B. S. Saini
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Diagnostic System ◽  
Ct Images ◽  
Active Contour Models ◽  
Segmentation Methods ◽  
Contour Models ◽  
Edge Based ◽  
Level Set Evolution

Precise segmentation of abdomen diseases like tumor, cyst and stone are crucial in the design of a computer aided diagnostic system. The complexity of shapes and similarity of texture of disease with the surrounding tissues makes the segmentation of abdomen related diseases much more challenging. Thus, this paper is devoted to the segmentation of abdomen diseases using active contour models. The active contour models are formulated using the level-set method. Edge-based Distance Regularized Level Set Evolution (DRLSE) and region based Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) are used for segmentation of various abdomen diseases. These segmentation methods are applied on 60 CT images (20 images each of tumor, cyst and stone). Comparative analysis shows that edge-based active contour models are able to segment abdomen disease more accurately than region-based level set active contour model.

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.

Heuristic Initialization for Active Contour Models in CT/MRI Image Processing

2015 ◽  
Vol 15 (03) ◽  
pp. 1550010
Author(s):  
Hao Liu ◽  
Hongbo Qian ◽  
Ning Dai ◽  
Jianning Zhao
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Heuristic Method ◽  
Initial Contour ◽  
Active Contour Models ◽  
Contour Model ◽  
Segmentation Approach ◽  
Edge Points ◽  
Contour Models ◽  
Level Set Evolution

It is an important segmentation approach of CT/MRI images to automatically extract contours in every slice using active contour models. The key point of the segmentation approach is to automatically construct initial contours for active contour models because any active contour model is sensitive to its initial contour. This paper presents an algorithm to construct such initial contours using a heuristic method. Assume that the contour in previous slice (previous contour) is accurate. The contour in the current slice (current contour) is constructed according to the previous contour using the way: Recognition and link of edge points of tissues according to the previous contour. The contour linking edge points is used as the initial contour of the distance regularized level set evolution (DRLSE) method and then an accurate contour can be extracted in the current slice.

EFFICIENT ACTIVE CONTOUR MODEL FOR MULTIPHASE SEGMENTATION WITH APPLICATION TO BRAIN MR IMAGES

2013 ◽  
Vol 27 (01) ◽  
pp. 1355001 ◽  
Author(s):  
YUNYUN YANG ◽  
YI ZHAO ◽  
BOYING WU
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Energy Functional ◽  
Energy Model ◽  
Mr Images ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Contour Model ◽  
New Energy ◽  
Brain Mr Images

In this paper, we propose an efficient active contour model for multiphase image segmentation in a variational level set formulation. By incorporating the globally convex segmentation idea and the split Bregman method into the multiphase formulation of the local and global intensity fitting energy model, our new model improved the original local and global intensity fitting energy model in the following aspects. First, we propose a new energy functional using the globally convex segmentation method to guarantee fast convergence. Second, we incorporate information from the edge into the energy functional by using a non-negative edge detector function to detect boundaries more easily. Third, instead of a constant value to control the influence of the local and global intensity fitting terms, we use a weight function varying with the locations of the image to balance the weights between the local and the global fitting terms dynamically. Lastly, the special structure of our energy functional enables us to apply the split Bregman method to minimize the energy much more efficiently. We have applied our model to synthetic images and real brain MR images with promising results. Experimental results demonstrate the efficiency and superiority of our model.

A FAST ADAPTIVE BALLOON ACTIVE CONTOUR FOR IMAGE SEGMENTATION

2017 ◽  
Vol 29 (01) ◽  
pp. 1750005
Author(s):  
Abdelhafid Nachour ◽  
Latifa Ouzizi ◽  
Youssef Aoura
Keyword(s):  
Active Contour ◽  
Complex Shape ◽  
Normal Force ◽  
Energy Functional ◽  
Initial Curve ◽  
Active Contour Models ◽  
New Models ◽  
Computer Vision Applications ◽  
Contour Models ◽  
Edge Based

Active Contour Models (ACM) have been widely used for segmentation in many computer vision applications. These models are defined by an energy functional attached to an initial curve that evolves under some constraints to extract desired objects in the image. New models are proposed, and existing techniques are investigated and improved in different domains. Among these ACM, Balloon ACM is an edge-based model that adds a normal force as constraint making the curve to have more dynamic behaviors and more effectiveness in detecting objects boundary. However, some problems have been pointed out including segmentation of complex shape and high runtime processing. In this paper, we develop a new method -called Fast Adaptive Balloon (FAB)- sufficient to segment complex shape with lower computational complexity. The proposed definition for balloon force achieves satisfactory segmentation performance compared with other ACMs using both synthetic and medical images in two dimension. The results demonstrate the accuracy and effectiveness in segmentation besides the convergence speed.

scholarly journals Automatic Segmentation of Abdominal Aortic Aneurism (AAA) By Using Active Contour Models

2020 ◽  
Vol 7 (1) ◽  
pp. 66-74
Author(s):  
Rifki Kosasih
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Active Contour ◽  
Aortic Wall ◽  
Active Contour Model ◽  
Automatic Segmentation ◽  
Accuracy Rate ◽  
Active Contour Models ◽  
Abdominal Aortic ◽  
Contour Models

Abdominal aortic aneurysm (AAA) is a disease that is caused by dilation of the aortic wall. Dilation of the aortic wall will affect the size of the diameter of lumen and the aorta. In this study we use T1 and T2 images on 4 patients with AAA which generated from MR Imaging to calculate the diameter of the abdominal aortic aneurysm (AAA). To calculate the diameter of lumen and the aorta, the first step is image registration using Laplacian eigenmap method. After that we propose an automatic segmentation method on region of the aorta by using active contour models to get the contour of lumen and the aorta. The last step,  we calculate the diameter of lumen and the aorta by using contour of lumen and the aorta. In our experiment, active contour model is very good method for segmentation AAA. In the result, our proposed model give the accuracy rate of lumen is 96.41% and accuracy rate of aorta is 95.22%. 

scholarly journals ACTIVE CONTOUR MODELS-BASED SEGMENTATION OF LEFT VENTRICLE IN ULTRASOUND IMAGES FOR DIFFERENT AXES VIEWS

2021 ◽  
Vol 21 (03) ◽  
pp. 2150031
Author(s):  
YANG ZHENG ◽  
ZHONGPING CHEN ◽  
JIAKE WANG ◽  
SHU JIANG ◽  
YU LIU
Keyword(s):  
Left Ventricle ◽  
Active Contour ◽  
Active Contour Model ◽  
Ultrasound Images ◽  
Similarity Coefficients ◽  
Active Contour Models ◽  
Shape Constraint ◽  
Chamber View ◽  
Contour Models ◽  
Axis View

Segmentation of the left ventricle in ultrasound images for viewing through different axes is a critical aspect. This paper proposes the development of novel active contour models with shape constraint to segment the left ventricle in three different axis views of the ultrasound images. The shapes observed in all the axis views of the left ventricle were not similar. According to the cardiac cycle, the valve opening in the end-diastolic phase influenced the left ventricle segmentation; hence, a shape constraint was embedded in the active contour model to keep ventricle’s shape, especially in the Apical long-axis view and Apical four-chamber view. Furthermore, for different axes views, diverse active contour models were proposed to fit each situation. The shape constraint in each function for different views exhibited a specific shape during the function iteration. In order to speed up the algorithm evolution, previous results were used for the initialization of the present active contour. We evaluated the proposed method on 57 patients with three different views: Apical long-axis view, Apical four-chamber view and Short-axis view at the papillary muscle level and obtained the Dice similarity coefficients of [Formula: see text], [Formula: see text] and [Formula: see text] and the Hausdorff distance metrics of [Formula: see text], [Formula: see text] and [Formula: see text], respectively. The qualitative and quantitative evaluations showed an advantage of our method in terms of segmentation accuracy.

Initialization Techniques for Chan-Vese Model with Thresholding

2012 ◽  
Vol 198-199 ◽  
pp. 227-232
Author(s):  
Hong Lan ◽  
Lu Zhang ◽  
Xuan Wang
Keyword(s):  
Active Contour ◽  
Considerable Increase ◽  
Level Set Methods ◽  
Energy Functional ◽  
Threshold Values ◽  
Object Boundary ◽  
Active Contour Models ◽  
Contour Models ◽  
Gray Level Histogram ◽  
Optimum Threshold

Chan-Vese model is one of classical active contour models for segmentation based on level set methods. It is the region-based model but in some cases it is still sensitive to the location of initial contours. The image thresholding is a simple but effective tool to separate objects from the background. In this paper, we integrate these two techniques and propose a new method to improve the initialization for Chan-Vese Model. First analyze the distribution of image gray level histogram and find the optimum threshold values, then set the model’s initial contours with thresholds and construct energy functional, lastly iterate the functional formulations until convergence to the object boundary. The method is tested on the plaque images and gives considerable increase in performance.

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