scholarly journals Automated Walks using Machine Learning for Segmentation

2013 ◽  
Author(s):  
Saurabh Vyas ◽  
Philippe Burlina ◽  
Dean Kleissas ◽  
Ryan Mukherjee
Keyword(s):  
Machine Learning ◽  
White Matter ◽  
White Matter Lesions ◽  
Brain Structures ◽  
Mr Images ◽  
K Nearest Neighbors ◽  
Canny Edge Detection ◽  
Random Walker ◽  
Automated Algorithm ◽  
Canny Edge

This paper describes an automated algorithm for segmentation of brain structures (CSF, white matter, and gray matter) in MR images. We employ machine learning, i.e. k-Nearest Neighbors, of features derived from k-means, Canny edge detection, and Tourist Walks to fully automate the seeding process of the Random Walker algorithm. We test our methods on a dataset of 12 diabetes patients with atrophy and varying degrees of white matter lesions provided by the MRBrainS13 Challenge, and find encouraging segmentation performance.

Morphometric analysis of white matter lesions in MR images: method and validation

1994 ◽  
Vol 13 (4) ◽  
pp. 716-724 ◽  
Author(s):  
A.P. Zijdenbos ◽  
B.M. Dawant ◽  
R.A. Margolin ◽  
A.C. Palmer
Keyword(s):  
White Matter ◽  
Morphometric Analysis ◽  
White Matter Lesions ◽  
Mr Images

scholarly journals Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yi Zhong ◽  
David Utriainen ◽  
Ying Wang ◽  
Yan Kang ◽  
E. Mark Haacke
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Gray Matter ◽  
Signal Intensity ◽  
High Spatial Frequency ◽  
Inversion Recovery ◽  
White Matter Hyperintensity ◽  
Mr Images ◽  
Automated Algorithm ◽  
Fluid Attenuated Inversion Recovery

White matter hyperintensities (WMH) seen on T2WI are a hallmark of multiple sclerosis (MS) as it indicates inflammation associated with the disease. Automatic detection of the WMH can be valuable in diagnosing and monitoring of treatment effectiveness. T2 fluid attenuated inversion recovery (FLAIR) MR images provided good contrast between the lesions and other tissue; however the signal intensity of gray matter tissue was close to the lesions in FLAIR images that may cause more false positives in the segment result. We developed and evaluated a tool for automated WMH detection only using high resolution 3D T2 fluid attenuated inversion recovery (FLAIR) MR images. We use a high spatial frequency suppression method to reduce the gray matter area signal intensity. We evaluate our method in 26 MS patients and 26 age matched health controls. The data from the automated algorithm showed good agreement with that from the manual segmentation. The linear correlation between these two approaches in comparing WMH volumes was found to beY=1.04X+1.74  (R2=0.96). The automated algorithm estimates the number, volume, and category of WMH.

Neuropsychologic Correlates of Brain White Matter Lesions Depicted on MR Images: 1921 Aberdeen Birth Cohort

Radiology ◽  
2001 ◽  
Vol 221 (1) ◽  
pp. 51-55 ◽  
Author(s):  
Steven A. Leaper ◽  
Alison D. Murray ◽  
Helen A. Lemmon ◽  
Roger T. Staff ◽  
Ian J. Deary ◽  
...  
Keyword(s):  
White Matter ◽  
Birth Cohort ◽  
White Matter Lesions ◽  
Mr Images ◽  
Brain White Matter

scholarly journals Classifying Dementia Using Local Binary Patterns from Different Regions in Magnetic Resonance Images

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Ketil Oppedal ◽  
Trygve Eftestøl ◽  
Kjersti Engan ◽  
Mona K. Beyer ◽  
Dag Aarsland
Keyword(s):  
White Matter ◽  
White Matter Lesions ◽  
Lewy Body Dementia ◽  
Local Binary Patterns ◽  
Magnetic Resonance Images ◽  
Mr Images ◽  
Computer Based ◽  
Disease Modifying Treatment ◽  
The Brain ◽  
Normal Controls

Dementia is an evolving challenge in society, and no disease-modifying treatment exists. Diagnosis can be demanding and MR imaging may aid as a noninvasive method to increase prediction accuracy. We explored the use of 2D local binary pattern (LBP) extracted from FLAIR and T1 MR images of the brain combined with a Random Forest classifier in an attempt to discern patients with Alzheimer's disease (AD), Lewy body dementia (LBD), and normal controls (NC). Analysis was conducted in areas with white matter lesions (WML) and all of white matter (WM). Results from 10-fold nested cross validation are reported as mean accuracy, precision, and recall with standard deviation in brackets. The best result we achieved was in the two-class problem NC versus AD + LBD with total accuracy of 0.98 (0.04). In the three-class problem AD versus LBD versus NC and the two-class problem AD versus LBD, we achieved 0.87 (0.08) and 0.74 (0.16), respectively. The performance using 3DT1 images was notably better than when using FLAIR images. The results from the WM region gave similar results as in the WML region. Our study demonstrates that LBP texture analysis in brain MR images can be successfully used for computer based dementia diagnosis.

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.

Vascular subcortical syndrome of aging

2004 ◽  
Vol 14 (4) ◽  
pp. 269-282 ◽  
Author(s):  
Mark Fish ◽  
Antony Bayer
Keyword(s):  
White Matter ◽  
White Matter Lesions ◽  
Brain Structures ◽  
Vascular Pathology ◽  
Rotterdam Study ◽  
Healthy Elderly ◽  
Subcortical Brain Structures ◽  
Physical Abilities ◽  
The Common ◽  
Healthy Elderly People

Important and common syndromes of old age, such as dementia, parkinsonism, falls, depression, and urinary incontinence, may be associated with vascular pathology affecting subcortical brain structures. The advent of neuroimaging has brought this more to the attention of clinicians and researchers, with increasing evidence that the common findings of white matter change, and small lacunar infarcts are not entirely benign, as previously assumed. Such lesions are common in elderly populations: only 5% of 1077 subjects aged between 60 and 90 from the Rotterdam study were found to be completely free of white matter lesions. Even in otherwise healthy elderly people, such lesions may impact on cognitive and physical abilities.

scholarly journals Computer-Assisted Segmentation of White Matter Lesions in 3D MR Images Using Support Vector Machine

2008 ◽  
Vol 15 (3) ◽  
pp. 300-313 ◽  
Author(s):  
Zhiqiang Lao ◽  
Dinggang Shen ◽  
Dengfeng Liu ◽  
Abbas F. Jawad ◽  
Elias R. Melhem ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
White Matter ◽  
White Matter Lesions ◽  
Support Vector ◽  
Computer Assisted ◽  
Mr Images

3D texture-based classification applied on brain white matter lesions on MR images

2016 ◽  
Author(s):  
Mariana Leite ◽  
David Gobbi ◽  
Marina Salluzi ◽  
Richard Frayne ◽  
Roberto Lotufo ◽  
...  
Keyword(s):  
White Matter ◽  
White Matter Lesions ◽  
Mr Images ◽  
3D Texture ◽  
Brain White Matter
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