Bone Segmentation of Magnetic Resonance Images by Gradient Vector Flow Active Contour with Atlas Based Centroid Forces

2010 ◽  
Author(s):  
Tong Kuan Chuah ◽  
Cheng Wei Lim ◽  
Chueh Loo Poh ◽  
Kenneth Sheah
Keyword(s):  
Magnetic Resonance ◽  
Active Contour ◽  
Magnetic Resonance Images ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
Bone Segmentation

Blood Vessel Segmentation in Complex-Valued Magnetic Resonance Images with Snake Active Contour Model

2010 ◽  
Vol 1 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Astri Handayani ◽  
Andriyan B. Suksmono ◽  
Tati L.R. Mengko ◽  
Akira Hirose
Keyword(s):  
Magnetic Resonance ◽  
Blood Vessel ◽  
Active Contour ◽  
Active Contour Model ◽  
Vessel Segmentation ◽  
Target Object ◽  
Magnetic Resonance Images ◽  
Blood Vessel Segmentation ◽  
Contour Model ◽  
Complex Valued

Accurate blood vessel segmentation plays a crucial role in non-invasive blood flow velocity measurement based on complex-valued magnetic resonance images. We propose a specific snake active contour model-based blood vessel segmentation framework for complex-valued magnetic resonance images. The proposed framework combines both magnitude and phase information from a complex-valued image representation to obtain an optimum segmentation result. Magnitude information of the complex-valued image provides a structural localization of the target object, while phase information identifies the existence of flowing matters within the object. Snake active contour model, which models the segmentation procedure as a force-balancing physical system, is being adopted as a framework for this work due to its interactive, dynamic, and customizable characteristics. Two snake-based segmentation models are developed to produce a more accurate segmentation result, namely the Model-constrained Gradient Vector Flow-snake (MC GVF-snake) and Stochastic-snake. MC GVF-snake elaborates a prior knowledge on common physical structure of the target object to restrict and guide the segmentation mechanism, while Stochastic-snake implements the simulated annealing stochastic procedure to produce improved segmentation accuracy. The developed segmentation framework has been evaluated on actual complex-valued MRI images, both in noise-free and noisy simulated conditions. Evaluation results indicate that both of the developed algorithms give an improved segmentation performance as well as increased robustness, in comparison to the conventional snake algorithm.

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 Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images

2020 ◽  
Vol 123 ◽  
pp. 103901
Author(s):  
Danilo Samuel Jodas ◽  
Maria Francisca Monteiro da Costa ◽  
Tiago A.A. Parreira ◽  
Aledir Silveira Pereira ◽  
João Manuel R.S. Tavares
Keyword(s):  
Magnetic Resonance ◽  
Carotid Artery ◽  
Active Contour ◽  
Active Contour Model ◽  
Magnetic Resonance Images ◽  
Proton Density ◽  
Distance Map ◽  
Contour Model
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