MR Images Segmentation Based on Coupled Geometrical Active Contour Model to Anisotropic Diffusion Filtering

2007 ◽  
Author(s):  
Foued Derraz ◽  
Abdelmalik Taleb-Ahmed ◽  
Azzeddine Chikh ◽  
Fethi Bereksi-Reguig
Keyword(s):  
Anisotropic Diffusion ◽  
Active Contour ◽  
Active Contour Model ◽  
Mr Images ◽  
Diffusion Filtering ◽  
Contour Model

Interactive 3-D Segmentation of the Frontal Lobe in 3.0T IR-FSPGR MR Images Using Fuzzy Rule-Based ACM

2003 ◽  
Vol 7 (2) ◽  
pp. 189-199 ◽  
Author(s):  
Yuji Fujiki ◽  
◽  
Syoji Kobashi ◽  
Mieko Matsui ◽  
Noriko Inoue ◽  
...  
Keyword(s):  
Frontal Lobe ◽  
Active Contour ◽  
Active Contour Model ◽  
Fuzzy Rule ◽  
Surface Rendering ◽  
Mr Images ◽  
Rule Based ◽  
Contour Model ◽  
Expert User ◽  
High Repeatability

Volumetry and surface rendering of the cerebral lobes are effective for evaluating lobar atrophy. This paper proposes a novel computer-aided system for segmenting the frontal lobe from 3-D human brain IR-FSPGR MR images with the fuzzy rule-based active contour model (ACM). The proposed system uses the criterial curves consisting of the central sulcus and the Sylvian fissure, which are given by users with the proposed graphical user interface. The user-given criterial curves are optimized by the fuzzy rule-based ACM. The fuzzy rule-based ACM can represent physicians’ knowledge with fuzzy if-then rules. With these optimized curves and the anterior and posterior commissures, the frontal lobe is segmented automatically. The experimental results on three healthy volunteers operated by an expert user and three beginner users showed that our system could segment the frontal lobe with high repeatability by any users and to any subjects.

EFFICIENT ACTIVE CONTOUR MODEL FOR MULTIPHASE SEGMENTATION WITH APPLICATION TO BRAIN MR IMAGES

2013 ◽  
Vol 27 (01) ◽  
pp. 1355001 ◽  
Author(s):  
YUNYUN YANG ◽  
YI ZHAO ◽  
BOYING WU
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Energy Functional ◽  
Energy Model ◽  
Mr Images ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Contour Model ◽  
New Energy ◽  
Brain Mr Images

In this paper, we propose an efficient active contour model for multiphase image segmentation in a variational level set formulation. By incorporating the globally convex segmentation idea and the split Bregman method into the multiphase formulation of the local and global intensity fitting energy model, our new model improved the original local and global intensity fitting energy model in the following aspects. First, we propose a new energy functional using the globally convex segmentation method to guarantee fast convergence. Second, we incorporate information from the edge into the energy functional by using a non-negative edge detector function to detect boundaries more easily. Third, instead of a constant value to control the influence of the local and global intensity fitting terms, we use a weight function varying with the locations of the image to balance the weights between the local and the global fitting terms dynamically. Lastly, the special structure of our energy functional enables us to apply the split Bregman method to minimize the energy much more efficiently. We have applied our model to synthetic images and real brain MR images with promising results. Experimental results demonstrate the efficiency and superiority of our model.

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