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.

FULLY AUTOMATED PEELING TECHNIQUE FOR T1-WEIGHTED, HIGH-QUALITY MR HEAD SCANS

2004 ◽  
Vol 04 (02) ◽  
pp. 141-156 ◽  
Author(s):  
RAVINDA G. N. MEEGAMA ◽  
JAGATH C. RAJAPAKSE
Keyword(s):  
White Matter ◽  
Healthy Subjects ◽  
Three Dimensional ◽  
Magnetic Resonance Images ◽  
Mr Images ◽  
3D Segmentation ◽  
High Quality ◽  
Anatomical Structures ◽  
Present Technique ◽  
The Brain

In order to conduct many non-intrusive clinical studies of the human brain, an accurate model that is capable of extracting the brain matter from magnetic resonance images (MRI) is required. We present a fully automated two-stage procedure to extract the brain matter accurately from a database of T1-weighted, high-quality MRI of healthy subjects. The procedure is initiated using a three dimensional (3D) segmentation process to separate the brain from other anatomical structures. The extracted brain is then subjected to an adaptive filter to remove cerebro-spinal fluid that fills sulcal cavities. The experiments clearly demonstrate the capability of the present technique in accurately peeling the brain. The accuracy of the results is tested using relative gray and white matter concentrations of both simulated and real MR images.

Spinal cord grey matter lesions in multiple sclerosis detected by post-mortem high field MR imaging

2008 ◽  
Vol 15 (2) ◽  
pp. 180-188 ◽  
Author(s):  
CP Gilmore ◽  
JJG Geurts ◽  
N Evangelou ◽  
JCJ Bot ◽  
RA van Schijndel ◽  
...  
Keyword(s):  
Spinal Cord ◽  
White Matter ◽  
Mr Imaging ◽  
High Field ◽  
Grey Matter ◽  
Great Majority ◽  
Proton Density ◽  
Post Mortem ◽  
Mr Images ◽  
The Brain

Background Post-mortem studies demonstrate extensive grey matter demyelination in MS, both in the brain and in the spinal cord. However the clinical significance of these plaques is unclear, largely because they are grossly underestimated by MR imaging at conventional field strengths. Indeed post-mortem MR studies suggest the great majority of lesions in the cerebral cortex go undetected, even when performed at high field. Similar studies have not been performed using post-mortem spinal cord material. Aim To assess the sensitivity of high field post-mortem MRI for detecting grey matter lesions in the spinal cord in MS. Methods Autopsy material was obtained from 11 MS cases and 2 controls. Proton Density-weighted images of this formalin-fixed material were acquired at 4.7Tesla before the tissue was sectioned and stained for Myelin Basic Protein. Both the tissue sections and the MR images were scored for grey matter and white matter plaques, with the readers of the MR images being blinded to the histopathology results. Results Our results indicate that post-mortem imaging at 4.7Tesla is highly sensitive for cord lesions, detecting 87% of white matter lesions and 73% of grey matter lesions. The MR changes were highly specific for demyelination, with all lesions scored on MRI corresponding to areas of demyelination. Conclusion Our work suggests that spinal cord grey matter lesions may be detected on MRI more readily than GM lesions in the brain, making the cord a promising site to study the functional consequences of grey matter demyelination in MS.

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 Peak ependymal cell stretch overlaps with the onset location of periventricular white matter lesions

2021 ◽  
Author(s):  
Valery Visser ◽  
Henry Rusinek ◽  
Johannes Weickenmeier
Keyword(s):  
White Matter ◽  
Ependymal Cell ◽  
White Matter Lesions ◽  
Magnetic Resonance Images ◽  
Ependymal Cells ◽  
Periventricular White Matter ◽  
Ventricular Wall ◽  
Imaging Data ◽  
Lateral Ventricles ◽  
Cell Stretch

Abstract Deep and periventricular white matter hyperintensities (dWMH/pvWMH) are bright appearing white matter tissue lesions in T2-weighted fluid attenuated inversion recovery magnetic resonance images and are frequent observations in the aging human brain. While early stages of these white matter lesions are only weakly associated with cognitive impairment, their progressive growth is a strong indicator for long-term functional decline. DWMHs are typically associated with vascular degeneration in diffuse white matter locations; for pvWMHs, however, no unifying theory exists to explain their consistent onset around the horns of the lateral ventricles. We use patient imaging data to create anatomically accurate finite element models of the lateral ventricles, white and gray matter, and cerebrospinal fluid, as well as to reconstruct their WMH volumes. We simulated the mechanical loading of the ependymal cells forming the primary brain-fluid interface, the ventricular wall, and its surrounding tissues at peak ventricular pressure during the hemodynamic cycle. We observe that both the maximum principal tissue strain and the largest ependymal cell stretch consistently localize in the anterior and posterior horns. Our simulations show that ependymal cells experience a loading state that causes the ventricular wall to be stretched thin. Moreover, we show that maximum wall loading coincides with the pvWMH locations observed in our patient scans. These results warrant further analysis of white matter pathology in the periventricular zone that includes a mechanics-driven deterioration model for the ventricular wall.

scholarly journals Relationship Between Type 2 Diabetes and White Matter Hyperintensity: A Systematic Review

2020 ◽  
Vol 11 ◽  
Author(s):  
Dan-Qiong Wang ◽  
Lei Wang ◽  
Miao-Miao Wei ◽  
Xiao-Shuang Xia ◽  
Xiao-Lin Tian ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
White Matter ◽  
Structural Changes ◽  
White Matter Lesions ◽  
Poor Performance ◽  
White Matter Hyperintensity ◽  
Diabetic Microangiopathy ◽  
The Relationship ◽  
The Brain

White matter (WM) disease is recognized as an important cause of cognitive decline and dementia. White matter lesions (WMLs) appear as white matter hyperintensities (WMH) on T2-weighted magnetic resonance imaging (MRI) scans of the brain. Previous studies have shown that type 2 diabetes (T2DM) is associated with WMH. In this review, we reviewed the literature on the relationship between T2DM and WMH in PubMed and Cochrane over the past five years and explored the possible links among the presence of T2DM, the course or complications of diabetes, and WMH. We found that: (1) Both from a macro- and micro-scopic point of view, most studies support the relationship of a larger WMH and a decrease in the integrity of WMH in T2DM; (2) From the relationship between brain structural changes and cognition in T2DM, the poor performance in memory, attention, and executive function tests associated with abnormal brain structure is consistent; (3) Diabetic microangiopathy or peripheral neuropathy may be associated with WMH, suggesting that the brain may be a target organ for T2DM microangiopathy; (4) Laboratory markers such as insulin resistance and fasting insulin levels were significantly associated with WMH. High HbA1c and high glucose variability were associated with WMH but not glycemic control.

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