Dynamic Active Contour Model for Size Independent Blood Vessel Lumen Segmentation and Quantification in High-Resolution Magnetic Resonance Images

2001 ◽  
pp. 264-273 ◽  
Author(s):  
Catherine Desbleds-Mansard ◽  
Alfred Anwander ◽  
Linda Chaabane ◽  
Maciej Orkisz ◽  
Bruno Neyran ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
High Resolution ◽  
Blood Vessel ◽  
Active Contour ◽  
Active Contour Model ◽  
Magnetic Resonance Images ◽  
Vessel Lumen ◽  
Contour Model ◽  
Lumen 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.

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

2012 ◽  
pp. 196-207
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.

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

Morphological Active Contour Model for Automatic Brain Tumor Extraction from Multimodal Magnetic Resonance Images

2021 ◽  
pp. 109296
Author(s):  
Zahra Shahvaran ◽  
Kamran Kazemi ◽  
Mahshid Fouladivanda ◽  
Mohammad Sadegh Helfroush ◽  
Olivier Godefroy ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Active Contour ◽  
Active Contour Model ◽  
Magnetic Resonance Images ◽  
Contour Model ◽  
Tumor Extraction

scholarly journals Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model

2018 ◽  
Vol 10 (9) ◽  
pp. 1459 ◽  
Author(s):  
Ying Sun ◽  
Xinchang Zhang ◽  
Xiaoyang Zhao ◽  
Qinchuan Xin
Keyword(s):  
High Resolution ◽  
Active Contour ◽  
Active Contour Model ◽  
Remote Sensing Data ◽  
Lidar Data ◽  
Boundary Extraction ◽  
Contour Model ◽  
Optical Images ◽  
Building Footprint ◽  
Salt And Pepper

Identifying and extracting building boundaries from remote sensing data has been one of the hot topics in photogrammetry for decades. The active contour model (ACM) is a robust segmentation method that has been widely used in building boundary extraction, but which often results in biased building boundary extraction due to tree and background mixtures. Although the classification methods can improve this efficiently by separating buildings from other objects, there are often ineluctable salt and pepper artifacts. In this paper, we combine the robust classification convolutional neural networks (CNN) and ACM to overcome the current limitations in algorithms for building boundary extraction. We conduct two types of experiments: the first integrates ACM into the CNN construction progress, whereas the second starts building footprint detection with a CNN and then uses ACM for post processing. Three level assessments conducted demonstrate that the proposed methods could efficiently extract building boundaries in five test scenes from two datasets. The achieved mean accuracies in terms of the F1 score for the first type (and the second type) of the experiment are 96.43 ± 3.34% (95.68 ± 3.22%), 88.60 ± 3.99% (89.06 ± 3.96%), and 91.62 ±1.61% (91.47 ± 2.58%) at the scene, object, and pixel levels, respectively. The combined CNN and ACM solutions were shown to be effective at extracting building boundaries from high-resolution optical images and LiDAR data.

scholarly journals An active contour model for brain magnetic resonance image segmentation based on multiple descriptors

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141878341 ◽  
Author(s):  
Chen Hong ◽  
Yu Xiaosheng ◽  
Wu Chengdong ◽  
Wu Jiahui
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Brain Tissue ◽  
Magnetic Resonance Image ◽  
Active Contour ◽  
Active Contour Model ◽  
Full Description ◽  
Contour Model ◽  
Brain Magnetic Resonance Image ◽  
The Brain

With the increasing use of surgical robots, robust and accurate segmentation techniques for brain tissue in the brain magnetic resonance image are needed to be embedded in the robot vision module. However, the brain magnetic resonance image segmentation results are often unsatisfactory because of noise and intensity inhomogeneity. To obtain accurate segmentation of brain tissue, one new multiphase active contour model, which is based on multiple descriptors mean, variance, and the local entropy, is proposed in this study. The model can bring about a more full description of local intensity distribution. Also, the entropy is introduced to improve the performance of robustness to noise of the algorithm. The segmentation and bias correction for brain magnetic resonance image can be simultaneously incorporated by introducing the bias factor in the proposed approach. At last, three experiments are carried out to test the performance of the method. The results in the experiments show that method proposed in this study performed better than most current methods in regard to accuracy and robustness. In addition, the bias-corrected images obtained by proposed method have better visual effect.

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