A Novel Method for Automatically Deciding Initial Contour of Level Set Segmentation

2012 ◽  
Vol 4 (17) ◽  
pp. 61-69
Author(s):  
Zhuan Song ◽  
Xiaowei He ◽  
Hai Geng
Keyword(s):  
Level Set ◽  
Initial Contour ◽  
Level Set Segmentation ◽  
Novel Method

scholarly journals 3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1256 ◽  
Author(s):  
Hassan A. Khalil ◽  
Saad Darwish ◽  
Yasmine M. Ibrahim ◽  
Osama F. Hassan
Keyword(s):  
Brain Tumor ◽  
Level Set ◽  
Size Structure ◽  
Initial Contour ◽  
Tumor Segmentation ◽  
3D Mri ◽  
Brain Tumor Segmentation ◽  
Dragonfly Algorithm ◽  
Level Set Segmentation ◽  
The Brain

Accurate brain tumor segmentation from 3D Magnetic Resonance Imaging (3D-MRI) is an important method for obtaining information required for diagnosis and disease therapy planning. Variation in the brain tumor’s size, structure, and form is one of the main challenges in tumor segmentation, and selecting the initial contour plays a significant role in reducing the segmentation error and the number of iterations in the level set method. To overcome this issue, this paper suggests a two-step dragonfly algorithm (DA) clustering technique to extract initial contour points accurately. The brain is extracted from the head in the preprocessing step, then tumor edges are extracted using the two-step DA, and these extracted edges are used as an initial contour for the MRI sequence. Lastly, the tumor region is extracted from all volume slices using a level set segmentation method. The results of applying the proposed technique on 3D-MRI images from the multimodal brain tumor segmentation challenge (BRATS) 2017 dataset show that the proposed method for brain tumor segmentation is comparable to the state-of-the-art methods.

Retraction Notice: Improved Level Set Segmentation Algorithm Based on Kernel Fuzzy Particles Swarm Optimization (KFPSO) Clustering for MRI Images

2018 ◽  
Vol 14 ◽  
Author(s):  
G. Elaiyaraja
Keyword(s):  
Medical Imaging ◽  
Level Set ◽  
Current Medical ◽  
Segmentation Algorithm ◽  
Scientific Publishing ◽  
Swarm Optimization ◽  
Original Source ◽  
Level Set Segmentation ◽  
Copyright Violation ◽  
Retraction Notice

The article entitled “Improved Level Set Segmentation Algorithm Based on Kernel Fuzzy Particles Swarm Optimization (KFPSO) Clustering for MRI Images”, by G. Elaiyaraja, P. Epsiba, N. Kumaratharan and G. Suresh, has been retracted. Kindly see Bentham Science Policy on Article retraction at the link given below: (https://www.benthamscience.com/journals/current-medical-imaging/author-guidelines/). This article has been retracted on the request of the Editor. The authors have plagiarized a paper that had already been published in the journal Current Medical Imaging (CMIM) (Formerly: Current Medical Imaging Reviews) 14(3), Page: 389-400. http://www.eurekaselect.com/149444. It is a pre-requisite for authors to declare explicitly that their work is original and has not been published elsewhere. Authors are advised to properly cite the original source to avoid plagiarism and copyright violation. As such this article represents a severe abuse of the scientific publishing system. Bentham Science Publishers takes a very strong view on this matter and apologizes to the readers of the journal for any inconvenience this may cause.

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