A GPU-Based Approach for Automatic Segmentation of White Matter Lesions

2017 ◽  
Vol 63 (4) ◽  
pp. 461-472 ◽  
Author(s):  
Ali Seydi Keçeli ◽  
Ahmet Burak Can ◽  
Aydin Kaya
Keyword(s):  
White Matter ◽  
Automatic Segmentation ◽  
White Matter Lesions

Automatic segmentation of brain white matter and white matter lesions in normal aging: comparison of five multispectral techniques

2012 ◽  
Vol 30 (2) ◽  
pp. 222-229 ◽  
Author(s):  
Maria del C. Valdés Hernández ◽  
Peter J. Gallacher ◽  
Mark E. Bastin ◽  
Natalie A. Royle ◽  
Susana Muñoz Maniega ◽  
...  
Keyword(s):  
White Matter ◽  
Automatic Segmentation ◽  
White Matter Lesions ◽  
Normal Aging ◽  
Brain White Matter

Automatic segmentation of different-sized white matter lesions by voxel probability estimation

2004 ◽  
Vol 8 (3) ◽  
pp. 205-215 ◽  
Author(s):  
P ANBEEK ◽  
K VINCKEN ◽  
M VANOSCH ◽  
R BISSCHOPS ◽  
J VANDERGROND
Keyword(s):  
White Matter ◽  
Automatic Segmentation ◽  
White Matter Lesions ◽  
Probability Estimation

scholarly journals Automatic Brain Tissue Segmentation of Multi-sequence MR Images Using Random Decision Forests

2013 ◽  
Author(s):  
Sérgio Pereira ◽  
Joana Festa ◽  
José António Mariz ◽  
Nuno Sousa ◽  
Carlos Silva
Keyword(s):  
White Matter ◽  
Automatic Segmentation ◽  
White Matter Lesions ◽  
Grand Challenge ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Random Decision Forests ◽  
Random Decision Forest ◽  
Segmentation Task ◽  
Mr Brain

This work is integrated in the MICCAI Grand Challenge: MR Brain Image Segmentation 2013. It aims for the automatic segmentation of brain into Cerebrospinal fluid (CSF), Gray matter (GM) and White matter (WM). The provided dataset contains patients with white matter lesions, which makes the segmentation task more challenging. The proposed algorithm uses multi-sequence MR images to extract meaningful features and learn a Random Decision Forest that classifies each voxel of the image. The results show that it is robust to the presence of the white matter lesions, and the metrics show that the overall results are competitive.

A Segmentation Method for Multiple Sclerosis White Matter Lesions on Conventional Magnetic Resonance Imaging Based on Kernel Fuzzy Clustering

2013 ◽  
Vol 339 ◽  
pp. 361-365 ◽  
Author(s):  
Yan Xiang ◽  
Jian Feng He ◽  
Lei Ma ◽  
San Li Yi ◽  
Jia Ping Xu
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Sclerosis ◽  
White Matter ◽  
Magnetic Resonance ◽  
Automatic Segmentation ◽  
White Matter Lesions ◽  
Region Growing ◽  
Conventional Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Conventional Magnetic Resonance

Multiple sclerosis (MS) is a chronic disease that affects the central nervous system and impacts substantially on patients. MS lesions are visible in conventional magnetic resonance imaging (cMRI) and the automatic segmentation of MS lesions enables the efficient processing of images for research studies and in clinical trials. A new method for the segmentation of MS white matter lesions (WML) on cMRI is presented in this paper. Firstly the Kernel Fuzzy C-Means Clustering (KFCM) is applied to the preprocessed T1-weight (T1-w) image for extracting the white matter (WM) region. Then region growing algorithm is applied to the WM region image to make a binary mask which is then superimposed on the corresponding T2-weight (T2-w) image to yield a masked image only containing WM structures and lesions. The KFCM is then reapplied to the masked image to obtain MS lesions. The testing results show that the proposed method is able to segment WML on cMRI automatically and effectively.

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