Skeleton-based region competition for automated gray matter and white matter segmentation of human brain MR images

2005 ◽  
Author(s):  
Yong Chu ◽  
Ya-Fang Chen ◽  
Min-Ying Su ◽  
Orhan Nalcioglu
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Gray Matter ◽  
Mr Images ◽  
Brain Mr Images

Segmentation of Rat Brain MR Images Using Artificial Neural Network Classifier

2013 ◽  
Author(s):  
K. N. Magdoom ◽  
Thomas H. Mareci ◽  
Malisa Sarntinoranont
Keyword(s):  
Neural Network ◽  
White Matter ◽  
Rat Brain ◽  
Gray Matter ◽  
Computational Models ◽  
Chemotherapeutic Agents ◽  
Mr Images ◽  
Artificial Neural Network Classifier ◽  
Brain Mr Images ◽  
The Brain

Recently MR image based computational models are being developed to assist targeted drug delivery in the brain by helping determine appropriate catheter position, drug dose among others to achieve the desired drug distribution [1–3]. Such a planning might be important to prevent damaging healthier tissues because many of the drugs (e.g. chemotherapeutic agents) are usually toxic and needs to be concentrated in specific regions of interest (e.g. tumor). However, for the image based model to make accurate predictions, it is important to segment the image and assign appropriate tissue properties such as hydraulic conductivity which are known to vary significantly within the brain. For example, it has been experimentally found that drugs injected into brain parenchyma get preferentially transported along the white matter tracts compared to the gray matter regions [4]. Segmenting MR images is a challenging task since the pixel intensities between different regions often overlap, hence traditional approaches based on thresholds might not provide reliable results. In this study, we used multi-layered perceptron (MLP) neural network to segment rat brain MR images into 3 different regions namely white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF).

BRAIN TUMOR DETECTION AND SEGMENTATION USING MULTISCALE INTUITIONISTIC FUZZY ROUGHNESS IN MR IMAGES

2019 ◽  
Vol 31 (03) ◽  
pp. 1950020 ◽  
Author(s):  
Yogita Dubey ◽  
Milind Mushrif ◽  
Kajal Mitra
Keyword(s):  
Cerebrospinal Fluid ◽  
Brain Tumor ◽  
White Matter ◽  
Rough Set ◽  
Gray Matter ◽  
Mr Images ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Cystic Component ◽  
Brain Mr Images

The magnetic resonance imaging technique is mostly used for visualizing and detecting brain tumor, which requires accurate segmentation of brain MR images into white matter, gray matter, cerebrospinal fluid, necrotic tissue, tumor, and edema. But brain image segmentation is a challenging task because of unknown noise and intensity inhomogeneity in brain MR images. This paper proposed a technique for the segmentation and the detection of a tumor, cystic component and edema in brain MR images using multiscale intuitionistic fuzzy roughness (MSIFR). Application of linear scale-space theory and intuitionistic fuzzy image representation deals with noise and intensity inhomogeneity in brain MR images. Intuitionistic fuzzy roughness calculated at proper scale is used to find optimum valley points for segmentation of brain MR images. The algorithm is applied to the real brain MR images from various hospitals and also to the benchmark set of the synthetic MR images from brainweb. The algorithm segments synthetic brain MR image into three regions, gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) and also separates tumor, cystic component and edema accurately in real brain MR images. The results of segmentation of proposed algorithm for synthetic images are compared with nonlocal fuzzy c-means (NLFCM), rough set based algorithms, intervalued possibilistic fuzzy c-means (IPFCM), robust modified Gaussian mixture model with rough set (RMGMMRS) and three algorithms, recursive bias corrected possibilistic fuzzy c-means (RBCPFCM), recursive bias corrected possibilistic neighborhood fuzzy c-means (RBCPNFCM) and recursive bias corrected separately weighted possibilistic neighborhood fuzzy c-means (RBCSPNFCM). The quantitative and qualitative evaluation demonstrates the superiority of the proposed algorithm.

scholarly journals Differentiation of Glucose Transport in Human Brain Gray and White Matter

2001 ◽  
Vol 21 (5) ◽  
pp. 483-492 ◽  
Author(s):  
Robin A. de Graaf ◽  
Jullie W. Pan ◽  
Frank Telang ◽  
Jing-Huei Lee ◽  
Peter Brown ◽  
...  
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Steady State ◽  
White Matter ◽  
Magnetic Resonance ◽  
Human Brain ◽  
Glucose Transport ◽  
Gray Matter ◽  
Transport Model ◽  
Transport Activity ◽  
Brain Glucose

Localized 1H nuclear magnetic resonance spectroscopy has been applied to determine human brain gray matter and white matter glucose transport kinetics by measuring the steady-state glucose concentration under normoglycemia and two levels of hyperglycemia. Nuclear magnetic resonance spectroscopic measurements were simultaneously performed on three 12-mL volumes, containing predominantly gray or white matter. The exact volume compositions were determined from quantitative T1 relaxation magnetic resonance images. The absolute brain glucose concentration as a function of the plasma glucose level was fitted with two kinetic transport models, based on standard (irreversible) or reversible Michaelis-Menten kinetics. The steady-state brain glucose levels were similar for cerebral gray and white matter, although the white matter levels were consistently 15% to 20% higher. The ratio of the maximum glucose transport rate, Vmax, to the cerebral metabolic utilization rate of glucose, CMRGlc, was 3.2 ± 0.10 and 3.9 ± 0.15 for gray matter and white matter using the standard transport model and 1.8 ± 0.10 and 2.2 ± 0.12 for gray matter and white matter using the reversible transport model. The Michaelis-Menten constant Km was 6.2 ± 0.85 and 7.3 ± 1.1 mmol/L for gray matter and white matter in the standard model and 1.1 ± 0.66 and 1.7 ± 0.88 mmol/L in the reversible model. Taking into account the threefold lower rate of CMRGlc in white matter, this finding suggests that blood–brain barrier glucose transport activity is lower by a similar amount in white matter. The regulation of glucose transport activity at the blood–brain barrier may be an important mechanism for maintaining glucose homeostasis throughout the cerebral cortex.

scholarly journals Long-term association of pregnancy and maternal brain structure: the Rotterdam Study

2021 ◽  
Author(s):  
Jurate Aleknaviciute ◽  
Tavia E. Evans ◽  
Elif Aribas ◽  
Merel W. de Vries ◽  
Eric A.P. Steegers ◽  
...  
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Gray Matter ◽  
Brain Structure ◽  
Gray Matter Volume ◽  
Rotterdam Study ◽  
Pregnancy And Childbirth ◽  
Matter Volume ◽  
Maternal Brain

ABSTRACTThe peripartum period is the highest risk interval for the onset or exacerbation of psychiatric illness in women’s lives. Notably, pregnancy and childbirth have been associated with short-term structural and functional changes in the maternal human brain. Yet the long-term effects of parity on maternal brain structure remain unknown. Therefore, we utilized a large population-based cohort to examine the association between parity and brain structure. In total, 2,835 women (mean age 65.2 years; all free from dementia, stroke, and cortical brain infarcts) from the Rotterdam Study underwent magnetic resonance imaging (1.5 T) between 2005 and 2015. Associations of parity with global and lobar brain tissue volumes, white matter microstructure, and markers of vascular brain disease were examined using regression models. We found that parity was associated with a larger global gray matter volume (β= 0.14, 95% CI = 0.09-0.19), a finding that persisted following adjustment for sociodemographic factors. A non-significant dose-dependent relationship was observed between a higher number of childbirths and larger gray matter volume. The gray matter volume association with parity was globally proportional across lobes. No associations were found regarding white matter volume or integrity, nor with markers of cerebral small vessel disease. The current findings indicate that pregnancy and childbirth are associated with robust long-term changes in brain structure involving larger global gray matter volume that persists for decades. Taken together, these data provide novel insight into the impact of motherhood on the human brain.

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