Paper Info
Title
Automatic 3D graph cuts for brain cortex segmentation in patients with focal cortical dysplasia.
Abstract
In patients with intractable epilepsy, focal cortical dysplasia (FCD) is the most frequent malformation of cortical development. Identification of subtle FCD lesions using brain MRI scans is very often based on the cortical thickness measurement, where brain cortex segmentation is required as a preprocessing step. However, the accuracy of the selected segmentation method can highly affect the final FCD lesion detection. In this work, we propose an improved graph cuts algorithm integrating Markov random field-based energy function for more accurate brain cortex MRI segmentation. Our method uses three-label graph cuts and preforms automatic 3D MRI brain cortex segmentation integrating intensity and boundary information. The performance of the method is tested on both simulated MR brain images with different noise levels and real patients with FCD lesions. Experimental quantitative segmentation results showed that the proposed method is effective, robust to noise and achieves higher accuracy than other popular brain MRI segmentation methods. The qualitative validation, visually verified by a medical expert, showed that the FCD lesions were segmented well as a part of the cortex, indicating increased thickness and cortical deformation. The proposed technique can be successfully used in this, as well as in many other clinical applications.
Year
DOI
Venue
2011
10.1109/IEMBS.2011.6091968
EMBC
Keywords
Field
DocType
automatic 3d graph cuts,brain cortex segmentation,cortical thickness measurement,image segmentation,markov processes,biomedical mri,fcd lesions,brain,intractable epilepsy,focal cortical dysplasia,segmentation integrating intensity,medical image processing,markov random field based energy function,brain mri scan,magnetic resonance imaging,three dimensional,edge detection,magnetic resonance image,brain imaging,graph cut,noise
Cortex (botany),Cut,Computer vision,Markov random field,Segmentation,Computer science,Image segmentation,Preprocessor,Artificial intelligence,Cortical dysplasia,Magnetic resonance imaging
Conference
Volume
ISSN
ISBN
2011
1557-170X
978-1-4244-4122-8
Citations 
PageRank 
References 
4
0.43
8
Authors
7
Name
Order
Citations
PageRank
Ivana Despotovic1363.54
Ief Segers240.43
Ljiljana Platisa3338.14
Ewout Vansteenkiste461.49
Aleksandra Pizurica51238102.29
Karel Deblaere640.77
Wilfried Philips71476124.85