scholarly journals Active contour driven by scalable local regional information on expandable kernel

2020 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Amir Faisal ◽  
Charnchai Pluempitiwiriyawej
Keyword(s):  
Active Contour ◽  
Active Contours ◽  
Smooth Boundary ◽  
Initial Position ◽  
The Other ◽  
Gaussian Kernel ◽  
Directional Selectivity ◽  
Noisy Image ◽  
Key Characteristics ◽  
Concave Boundary

An active contour that uses the pixel’s intensity on a set of expandable kernels along the propagating contour for image segmentation is presented in this paper. The objective is this study is to employ the scalable kernels to attract the contour to meet the desired boundary. The key characteristics of this scheme is that the kernels gradually expand to find an object’s boundary. So this scheme could penetrate to the concave boundary more effective and efficient than some other schemes. If a Gaussian kernel is applied, it could trace the object with a blurred or smooth boundary. Moreover, the directional selectivity feature enables in capturing two edge’s types with just one initial position. Its performance showed more desirable segmentation outcomes compared to the other existing active contours using regional information when segmenting the noisy image and the non-uniform (or heterogeneous) textures. Meanwhile, the level set implementation enables topological flexibility to our active contour scheme.

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.

Automatic segmentation of medical images using a novel Harris Hawk optimization method and an active contour model

2021 ◽  
pp. 1-19
Author(s):  
Maria Tamoor ◽  
Irfan Younas
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Heart Diseases ◽  
Medical Image Segmentation ◽  
Gaussian Kernel ◽  
Initial Contour ◽  
Automated Method ◽  
Contour Model

Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, and ill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard cardiac magnetic resonance dataset (ACDC, MICCAI 2017) with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experimental results show that the proposed method can effectively segment the region of interest and produce superior segmentation results for skin (overall Dice Score 0.90) and cardiac dataset (overall Dice Score 0.93), as compared to other state-of-the-art algorithms.

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 SEGMENTASI CITRA THORAX PARU-PARU MANUSIA DARI SINAR-X MENGGUNAKAN METODE KONTUR AKTIF

2019 ◽  
Vol 4 (2) ◽  
pp. 8-10
Author(s):  
Sintha Syaputri ◽  
Zulkarnain
Keyword(s):  
Active Contour ◽  
Active Contours ◽  
Contour Method ◽  
Image Objects ◽  
Contour Segmentation ◽  
Segmentation Process ◽  
Coordinate Position ◽  
Image Improvement ◽  
The Right

Research on medical image objects in the form of lung images of thoracic X-Rayis increasingly being developed because the information contained in medical images is used to analyze and determine the shape of the lungs. The process of normalization and image improvement is needed and continued with the segmentation process using the right method. The active snake contour method is used because it is resistant to the noise around the object. The research has been usedthe Matlab software GUI program version R2015a. The image through the initial preprocessing stage is converted into a grayscale image. The segmentation process used after the initialization process in the form of a small circle curve placed of the object to be segmented and the determination position of the active contour or detemination of the active parameters of the contour. Determination of the value active contour parameters greatly influences the results of segmentation and influences the direction of active contour movement. If the active coordinate position of the contour is outside the area to be segmented it will cause active contours to move away from the object. Validation the level of accuracy of segmentation results is done by comparing the results of the snake active contour segmentation to the results of manual segmentationused MSE method

Introduction

2018 ◽  
pp. 1-24
Author(s):  
Derek Hird ◽  
Geng Song
Keyword(s):  
Detailed Analysis ◽  
Chinese Women ◽  
The Other ◽  
Business Markets ◽  
Trading Systems ◽  
Chinese Men ◽  
Scholarly Literature ◽  
Romantic Involvement ◽  
Key Characteristics ◽  
Chinese Masculinity

This chapter outlines transnational masculinities as a field of Study, and scholarship on transnationally inflected representations of Chinese masculinity and transnationally mobile Chinese men. It identifies three key key characteristics in the scholarly literature on Chinese masculinities in the context of globalization. First, the concept of cosmopolitanism is being increasingly used to explore the localization of globally circulating ideas and images in Chinese masculinities. Second, China’s integration with global financial and trading systems, which has been particularly pronounced since the 1990s, has forced the historically dominant intellectual or scholar-official (shi士‎) class to reconcile itself with the business activities traditionally carried out by the merchant (shang商‎) class. Third, the transnational circulation of models of emotionally expressive and caring fatherhood is significantly influencing Chinese discourses and practices of fathering. Through a detailed analysis of the other chapters in the volume, this chapter argues that it is possible to identify five broad patterns in the transformations of Chinese transnational masculinities: the embrace of localized cosmopolitan masculinities that are part-founded on historical notions and practices of Chinese masculinity; the enmeshment of intellectuals in business markets; emotionally engaged styles of fathering and intimate partnership; romantic involvement with non-Chinese women; and widespread anxiety and sensitivity about perceptions of Chinese masculinity. This chapter concludes that Chinese men are not unique in having to face such issues in transnational contexts; but, as the other chapters in this volume demonstrate, they negotiate them in unique—yet explainable—ways.

scholarly journals A Fast Region-Based Segmentation Model with Gaussian Kernel of Fractional Order

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Bo Chen ◽  
Qing-Hua Zou ◽  
Wen-Sheng Chen ◽  
Yan Li
Keyword(s):  
Fractional Order ◽  
Energy Function ◽  
Active Contour ◽  
Active Contour Model ◽  
Gaussian Kernel ◽  
Proposed Model ◽  
Function Expression ◽  
Local Mean ◽  
Geometric Active Contour ◽  
Evolution Curve

By summarizing some classical active contour models from the view of level set representation, a simple energy function expression with the Gaussian kernel of fractional order is proposed, and then a novel region-based geometric active contour model is established. In this proposed model, the energy function with value of [−1, 1] is built, the local mean and global mean of the inside and outside of the evolution curve are employed, and the segmentation results are obtained by controlling the expansion and contraction of the evolution curve. The model is simple and easy to implement; it can also protect weak edges because of considering more statistical information. Experimental results on synthetic and natural images show that the proposed model is much more effective in dealing with the images with weak or blurred edges, and it takes less time.

scholarly journals Fusion of External and Internal Prior Information for the Removal of Gaussian Noise in Images

2020 ◽  
Vol 6 (10) ◽  
pp. 103
Author(s):  
Ali S. Awad
Keyword(s):  
Gaussian Noise ◽  
Prior Information ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
The Other ◽  
Noisy Image ◽  
Simulation Experiments ◽  
Two Dimensional Image ◽  
Pixel Location ◽  
Image Pixels

In this paper, a new method for the removal of Gaussian noise based on two types of prior information is described. The first type of prior information is internal, based on the similarities between the pixels in the noisy image, and the other is external, based on the index or pixel location in the image. The proposed method focuses on leveraging these two types of prior information to obtain tangible results. To this end, very similar patches are collected from the noisy image. This is done by sorting the image pixels in ascending order and then placing them in consecutive rows in a new two-dimensional image. Henceforth, a principal component analysis is applied on the patch matrix to help remove the small noisy components. Since the restored pixels are similar or close in values to those in the clean image, it is preferable to arrange them using indices similar to those of the clean pixels. Simulation experiments show that outstanding results are achieved, compared to other known methods, either in terms of image visual quality or peak signal to noise ratio. Specifically, once the proper indices are used, the proposed method achieves PSNR value better than the other well-known methods by >1.5 dB in all the simulation experiments.

Cooperation and Collaboration Using Computer-Mediated Communication

2002 ◽  
Vol 26 (3) ◽  
pp. 325-347 ◽  
Author(s):  
Lesley G. Hathorn ◽  
Albert L. Ingram
Keyword(s):  
Computer Mediated Communication ◽  
Cost Benefit ◽  
Point Of View ◽  
The Other ◽  
Mediated Communication ◽  
Collaborative Groups ◽  
Trade Offs ◽  
Computer Mediated ◽  
Key Characteristics ◽  
The Cost

This study operationally defined and measured collaboration and compared the products and structure of collaborative groups that used computer-mediated communication. Key characteristics of collaboration selected from the literature were interdependence, synthesis, and independence, and a model for evaluating these characteristics was developed. All communication in this study occurred via asynchronous computer-mediated communication, using a threaded Web discussion. Participants in the study were graduate students, studying the same course with the same instructor at two venues. The students were divided into small groups from one or both venues, and four of these groups were studied. All students were given a problem to solve involving the cost-benefit trade-offs of distance education. The groups received different instructions. Two of them were told to collaborate on a solution, and the other two were told to select a role and discuss the problem from that point of view. Groups that were instructed to collaborate were more collaborative, but they produced a solution of a lower quality than the other groups. No conclusions could be drawn from the results on the structure of the groups. The role of collaboration in problem solving is discussed along with methods for creating more effective collaboration.

scholarly journals Dependence of Shape-Based Descriptors and Mass Segmentation Areas on Initial Contour Placement Using the Chan-Vese Method on Digital Mammograms

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
S. N. Acho ◽  
W. I. D. Rae
Keyword(s):  
Level Set ◽  
Active Contours ◽  
Initial Level ◽  
Pearson Correlation ◽  
Region Of Interest ◽  
Initial Position ◽  
Initial Contour ◽  
Boundary Information ◽  
Mass Segmentation ◽  
Mass Lesions

Variation in signal intensity within mass lesions and missing boundary information are intensity inhomogeneities inherent in digital mammograms. These inhomogeneities render the performance of a deformable contour susceptible to the location of its initial position and may lead to poor segmentation results for these images. We investigate the dependence of shape-based descriptors and mass segmentation areas on initial contour placement with the Chan-Vese segmentation method and compare these results to the active contours with selective local or global segmentation model. For each mass lesion, final contours were obtained by propagation of a proposed initial level set contour and by propagation of a manually drawn contour enclosing the region of interest. Differences in shape-based descriptors were quantified using absolute percentage differences, Euclidean distances, and Bland-Altman analysis. Segmented areas were evaluated with the area overlap measure. Differences were dependent upon the characteristics of the mass margins. Boundary moments presented large percentage differences. Pearson correlation analysis showed statistically significant correlations between shape-based descriptors from both initial locations. In conclusion, boundary moments of digital mass lesions are sensitive to the placement of initial level set contours while shape-based descriptors such as Fourier descriptors, shape convexity, and shape rectangularity exhibit a certain degree of robustness to changes in the location of the initial level set contours for both segmentation algorithms.

scholarly journals Multiple Active Contours Driven by Particle Swarm Optimization for Cardiac Medical Image Segmentation

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
I. Cruz-Aceves ◽  
J. G. Aviña-Cervantes ◽  
J. M. López-Hernández ◽  
S. E. González-Reyna
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contours ◽  
Active Contour Model ◽  
Particle Swarm ◽  
Magnetic Resonance Images ◽  
Swarm Optimization ◽  
Contour Model

This paper presents a novel image segmentation method based on multiple active contours driven by particle swarm optimization (MACPSO). The proposed method uses particle swarm optimization over a polar coordinate system to increase the energy-minimizing capability with respect to the traditional active contour model. In the first stage, to evaluate the robustness of the proposed method, a set of synthetic images containing objects with several concavities and Gaussian noise is presented. Subsequently, MACPSO is used to segment the human heart and the human left ventricle from datasets of sequential computed tomography and magnetic resonance images, respectively. Finally, to assess the performance of the medical image segmentations with respect to regions outlined by experts and by the graph cut method objectively and quantifiably, a set of distance and similarity metrics has been adopted. The experimental results demonstrate that MACPSO outperforms the traditional active contour model in terms of segmentation accuracy and stability.

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