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

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.

Download Full-text

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

10.54294/azta8g ◽

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.

Download Full-text

Automated Walks using Machine Learning for Segmentation

10.54294/1ktciq ◽

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.

Download Full-text

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

Academic Radiology ◽

10.1016/j.acra.2007.10.012 ◽

2008 ◽

Vol 15 (3) ◽

pp. 300-313 ◽

Cited By ~ 155

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

Download Full-text

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

10.1117/12.2216285 ◽

2016 ◽

Cited By ~ 2

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

Download Full-text

Stratified mixture modeling for segmentation of white-matter lesions in brain MR images

NeuroImage ◽

10.1016/j.neuroimage.2015.09.047 ◽

2016 ◽

Vol 124 ◽

pp. 1031-1043 ◽

Cited By ~ 10

Author(s):

Alfiia Galimzianova ◽

Franjo Pernuš ◽

Boštjan Likar ◽

Žiga Špiclin

Keyword(s):

White Matter ◽

White Matter Lesions ◽

Mixture Modeling ◽

Mr Images ◽

Brain Mr Images

Download Full-text

Automatic segmentation of white matter lesions in T1-weighted brain MR images

Proceedings IEEE International Symposium on Biomedical Imaging ◽

10.1109/isbi.2002.1029241 ◽

2003 ◽

Cited By ~ 1

Author(s):

Songyang Yu ◽

D.L. Pham ◽

Dinggang Shen ◽

E.H. Herskovits ◽

S.M. Resnick ◽

...

Keyword(s):

White Matter ◽

Automatic Segmentation ◽

White Matter Lesions ◽

Mr Images ◽

Brain Mr Images

Download Full-text

Novel Automated Method for the Detection of White Matter Hyperintensities in Brain Multispectral MR Images

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405614666180801112844 ◽

2020 ◽

Vol 16 (5) ◽

pp. 469-478

Author(s):

Hsian-Min Chen ◽

Clayton Chi-Chang Chen ◽

Hsin Che Wang ◽

Yung-Chieh Chang ◽

Kuan-Jung Pan ◽

...

Keyword(s):

White Matter ◽

Anomaly Detection ◽

White Matter Hyperintensities ◽

Similarity Index ◽

White Matter Lesions ◽

Cerebral White Matter ◽

Mr Images ◽

Detection Algorithms ◽

Automated Method ◽

Brain Mr Images

Background: According to the Standards for Reporting Vascular Changes on Neuroimaging, White Matter Hyperintensities (WMHs) are cerebral white matter lesions that are characterized by abnormal tissues of variable sizes and appear hyperintense in T2-weighted Magnetic Resonance (MR) measurements without cavitation (i.e., their tissue signals differ from those of Cerebrospinal Fluid or CSF). Such abnormal tissue regions are typically observed in the MR images of brains of healthy older adults and are associated with a number of geriatric neurodegenerative diseases. Explanations of the exact causes and mechanisms of these diseases remain inconclusive. Moreover, WMHs are typically identified by visual assessment and manual examination, both of which require considerable time. This brings up a need of developing a method for detecting WMHs more objectively and enabling patients to be treated early. As a consequence, damages on nerve cells can be limited and the severity of patients’ conditions can be contained. Aims: This paper presents a computer-aided technique for automatically detecting and segmenting anomalies in MR images. Methods: The method has two steps: (1) a Band Expansion Process (BEP) to expand the dimensions of brain MR images nonlinearly and (2) anomaly detection algorithms to detect WMHs. Synthesized MR images provided by BrainWeb were used as benchmarks against which the detection performance of the algorithms was determined. Results: The most notable findings are as follows: Firstly, compared with the other anomaly detection algorithms and the Lesion Segmentation Tool (LST), BEP-anomaly detection is shown to be the most effective in detecting WMHs. Secondly, across all levels of background noise and inhomogeneity, the mean Similarity Index (SI) produced by our proposed algorithm is higher than that produced by LST, indicating that the algorithm is more effective than LST in segmenting WMHs from brain MR images. Conclusion: Experimental results demonstrated a significantly high accuracy of the BEP-K/R-RX method in detection of synthetic brain MS lesion data. In the meantime, it also effectively enhances the detection of brain lesions.

Download Full-text