GPU-Based Active Contour Segmentation Using Gradient Vector Flow

2006 ◽  
pp. 191-201 ◽  
Author(s):  
Zhiyu He ◽  
Falko Kuester
Keyword(s):  
Active Contour ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
Contour Segmentation

Active Contour Model with Edge Flow Vector for Texture Segmentation

2015 ◽  
Vol 781 ◽  
pp. 511-514
Author(s):  
Tanunchai Boonnuk ◽  
Sanun Srisuk ◽  
Thanwa Sripramong
Keyword(s):  
Medical Imaging ◽  
Active Contour ◽  
Active Contour Model ◽  
Texture Segmentation ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
Texture Image ◽  
Contour Model ◽  
Flow Vector ◽  
Edge Flow

In this paper, we propose effective method for texture segmentation using active contour model with edge flow vector. This technique was applied from previous active contour model that uses gradient vector flow as external force. It was observed that our method provided better results for texture segmentation while a traditional active contour model and active contour model with gradient vector flow were not capable to be used with texture image. Thus, texture image such as medical imaging can be identified using active contour model with edge flow vector.

scholarly journals A Prostate MRI Segmentation Tool Based on Active Contour Models Using a Gradient Vector Flow

2020 ◽  
Vol 10 (18) ◽  
pp. 6163 ◽  
Author(s):  
Joaquín Rodríguez ◽  
Gilberto Ochoa-Ruiz ◽  
Christian Mata
Keyword(s):  
Active Contour ◽  
Automatic Segmentation ◽  
Prostate Gland ◽  
Manual Segmentation ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
Active Contour Models ◽  
Prostate Segmentation ◽  
Segmentation Process ◽  
Contour Models

Medical support systems used to assist in the diagnosis of prostate lesions generally related to prostate segmentation is one of the majors focus of interest in recent literature. The main problem encountered in the diagnosis of a prostate study is the localization of a Regions of Interest (ROI) containing a tumor tissue. In this paper, a new GUI tool based on a semi-automatic prostate segmentation is presented. The main rationale behind this tool and the focus of this article is facilitate the time consuming segmentation process used for annotating images in the clinical practice, enabling the radiologists to use novel and easy to use semi-automatic segmentation techniques instead of manual segmentation. In this work, a detailed specification of the proposed segmentation algorithm using an Active Contour Models (ACM) aided with a Gradient Vector Flow (GVF) component is defined. The purpose is to help the manual segmentation process of the main ROIs of prostate gland zones. Finally, an experimental case of use and a discussion part of the results are presented.

scholarly journals Improved Algorithm for Gradient Vector Flow Based Active Contour Model Using Global and Local Information

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Jianhui Zhao ◽  
Bingyu Chen ◽  
Mingui Sun ◽  
Wenyan Jia ◽  
Zhiyong Yuan
Keyword(s):  
Force Field ◽  
External Force ◽  
Active Contour ◽  
Active Contours ◽  
Active Contour Model ◽  
Computational Cost ◽  
Local Information ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
External Force Field

Active contour models are used to extract object boundary from digital image, but there is poor convergence for the targets with deep concavities. We proposed an improved approach based on existing gradient vector flow methods. Main contributions of this paper are a new algorithm to determine the false part of active contour with higher accuracy from the global force of gradient vector flow and a new algorithm to update the external force field together with the local information of magnetostatic force. Our method has a semidynamic external force field, which is adjusted only when the false active contour exists. Thus, active contours have more chances to approximate the complex boundary, while the computational cost is limited effectively. The new algorithm is tested on irregular shapes and then on real images such as MRI and ultrasound medical data. Experimental results illustrate the efficiency of our method, and the computational complexity is also analyzed.

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