Energy Update Restricted Chan–Vese Model for Tumor Extraction from MRI of Human Head Scans

2017 ◽  
Vol 15 (01) ◽  
pp. 1750081
Author(s):  
T. Kalaiselvi ◽  
S. Karthigai Selvi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Active Contour ◽  
Active Contour Model ◽  
Human Head ◽  
Multiphase Model ◽  
Resonance Imaging ◽  
Qualitative And Quantitative ◽  
Contour Model ◽  
Tumor Extraction

This research paper proposed a newer strategic method for the extraction of tumor from magnetic resonance imaging scans by employing a region-based active contour model (ACM). The earlier methods have applied the process of contour initialization randomly and updating the energy of the contour at every iteration. The proposed method used wavelet-based feature set to initiate the contour and restricts the energy update procedure. The efficiency of the presented technique in terms of tumor extraction is measured through qualitative and quantitative measures further compared with its counterparts Vese–Chan multiphase model, ACM and Selective binary. Gaussian filtering regularized level set and non active contour based models.

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

Quantification of Lymphedema in a Rat Model by 3D-Active Contour Segmentation by Magnetic Resonance Imaging

2012 ◽  
Vol 10 (1) ◽  
pp. 25-29 ◽  
Author(s):  
Tanja Sommer ◽  
Martin Meier ◽  
Frank Bruns ◽  
Reinhard Pabst ◽  
Gerhard Breves ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Rat Model ◽  
Active Contour ◽  
Resonance Imaging ◽  
Contour 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.

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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