A fast and robust segmentation of magnetic resonance brain images using a combination of the pyramidal approach and level set method

2016 ◽  
Vol 26 (4) ◽  
pp. 243-253
Author(s):  
Fatima Zohra Belgrana ◽  
Nacéra Benamrane
Keyword(s):  
Magnetic Resonance ◽  
Level Set Method ◽  
Level Set ◽  
Brain Images ◽  
Robust Segmentation

scholarly journals Skull-stripping magnetic resonance brain images using a model-based level set

NeuroImage ◽  
2006 ◽  
Vol 32 (1) ◽  
pp. 79-92 ◽  
Author(s):  
Audrey H. Zhuang ◽  
Daniel J. Valentino ◽  
Arthur W. Toga
Keyword(s):  
Magnetic Resonance ◽  
Level Set ◽  
Brain Images ◽  
Model Based ◽  
Skull Stripping

Inhomogeneous Brain Magnetic Resonance Images Segmentation Using a Novel Double Level Set Method

2020 ◽  
Vol 10 (10) ◽  
pp. 2452-2458
Author(s):  
Jianhua Song ◽  
Shuqin Li
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Level Set Method ◽  
Level Set ◽  
Magnetic Resonance Images ◽  
Heaviside Function ◽  
Mr Image ◽  
Level Set Function ◽  
Clustering Model ◽  
Mr Image Segmentation

Magnetic resonance (MR) image segmentation plays an important role in the clinical diagnosis and pathological analysis of brain diseases, and has become a focus in the field of medical image processing. However, MR image segmentation is also a complex task because it is easily corrupted by inhomogeneous intensity and noise during the process of imaging. In this paper, we use double level set function to replace single level set of the data energy fitting model and propose a model based on Legendre polynomial and Heaviside function, which is used to segment brain magnetic resonance images. The double level set method (DLSM) can extract simultaneously the white matter (WM) and gray matter (GM) of brain tissue and ensure the robustness of level set initialization. Moreover, the bias field caused by intensity inhomogeneity is represented by a set of smooth basis functions, which can satisfy its property of slow variety. Finally, compared with the local intensity clustering model and multiplicative intrinsic component optimization model, both visual and objective results can prove the superior of the proposed DLSM model, and the computational speed is faster.

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