Non-parametric Brain Tissues Segmentation via a Parallel Architecture of CNNs

2019 ◽  
pp. 216-226
Author(s):  
Dante Mújica-Vargas ◽  
Alicia Martínez ◽  
Manuel Matuz-Cruz ◽  
Antonio Luna-Alvarez ◽  
Mildred Morales-Xicohtencatl
Keyword(s):  
Parallel Architecture ◽  
Non Parametric ◽  
Brain Tissues

The Recent Revolution in High Performance Computing

MRS Bulletin ◽  
1997 ◽  
Vol 22 (10) ◽  
pp. 5-6
Author(s):  
Horst D. Simon
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
New Technologies ◽  
New Technology ◽  
Parallel Architecture ◽  
Time Frame ◽  
Good News ◽  
Computing Industry ◽  
Time And Energy ◽  
Performance Computing

Recent events in the high-performance computing industry have concerned scientists and the general public regarding a crisis or a lack of leadership in the field. That concern is understandable considering the industry's history from 1993 to 1996. Cray Research, the historic leader in supercomputing technology, was unable to survive financially as an independent company and was acquired by Silicon Graphics. Two ambitious new companies that introduced new technologies in the late 1980s and early 1990s—Thinking Machines and Kendall Square Research—were commercial failures and went out of business. And Intel, which introduced its Paragon supercomputer in 1994, discontinued production only two years later.During the same time frame, scientists who had finished the laborious task of writing scientific codes to run on vector parallel supercomputers learned that those codes would have to be rewritten if they were to run on the next-generation, highly parallel architecture. Scientists who are not yet involved in high-performance computing are understandably hesitant about committing their time and energy to such an apparently unstable enterprise.However, beneath the commercial chaos of the last several years, a technological revolution has been occurring. The good news is that the revolution is over, leading to five to ten years of predictable stability, steady improvements in system performance, and increased productivity for scientific applications. It is time for scientists who were sitting on the fence to jump in and reap the benefits of the new technology.

ESTIMATION OF PRESSURE DROP FOR NON-NEWTONIAN LIQUID FLOW THROUGH BENDS USING ADAPTIVE NON-PARAMETRIC MODEL

2020 ◽  
Vol 47 (1) ◽  
pp. 59-69
Author(s):  
Suman Debnath ◽  
Anirban Banik ◽  
Tarun Kanti Bandyopadhyay ◽  
Mrinmoy Majumder ◽  
Apu Kumar Saha
Keyword(s):  
Pressure Drop ◽  
Liquid Flow ◽  
Parametric Model ◽  
Newtonian Liquid ◽  
Flow Through ◽  
Non Parametric

Characterization of Ischemic Stroke in CT Images using Image Processing

2017 ◽  
Vol 7 (9) ◽  
pp. 18
Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Classification Accuracy ◽  
Ct Images ◽  
Gray Level ◽  
Level Variation ◽  
Interactive Data ◽  
The Brain ◽  
Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

Contrast-Enhanced MRI with Magnetization Transfer Effect in the Imaging of Brain Metastatic Lesions

2017 ◽  
pp. 8-17
Author(s):  
A. A. Ermakova ◽  
O. Yu. Borodin ◽  
M. Yu. Sannikov ◽  
S. D. Koval ◽  
V. Yu. Usov
Keyword(s):  
Comparative Analysis ◽  
Roc Analysis ◽  
Magnetization Transfer ◽  
Metastatic Lesion ◽  
Transfer Effect ◽  
Wilcoxon Test ◽  
Post Contrast ◽  
Metastatic Lesions ◽  
The Brain ◽  
Non Parametric

Purpose: to investigate the diagnostic opportunities of contrast  magnetic resonance imaging with the effect of magnetization transfer effect in the diagnosis of focal metastatic lesions in the brain.Materials and methods.Images of contrast MRI of the brain of 16  patients (mean age 49 ± 18.5 years) were analysed. Diagnosis of  the direction is focal brain lesion. All MRI studies were carried out  using the Toshiba Titan Octave with magnetic field of 1.5 T. The  contrast agent is “Magnevist” at concentration of 0.2 ml/kg was  used. After contrasting process two T1-weighted studies were  performed: without T1-SE magnetization transfer with parameters of pulse: TR = 540 ms, TE = 12 ms, DFOV = 24 sm, MX = 320 × 224  and with magnetization transfer – T1-SE-MTC with parameters of pulse: ΔF = −210 Hz, FA(МТС) = 600°, TR = 700 ms, TE = 10 ms,  DFOV = 23.9 sm, MX = 320 x 224. For each detected metastatic  lesion, a contrast-to-brain ratio (CBR) was calculated. Comparative  analysis of CBR values was carried out using a non-parametric  Wilcoxon test at a significance level p < 0.05. To evaluate the  sensitivity and specificity of the techniques in the detection of  metastatic foci (T1-SE and T1-SE-MTC), ROC analysis was used. The sample is divided into groups: 1 group is foci ≤5 mm in size, 2  group is foci from 6 to 10 mm, and 3 group is foci >10 mm. Results.Comparative analysis of CBR using non-parametric Wilcoxon test showed that the values of the CBR on T1-weighted  images with magnetization transfer are significantly higher (p  <0.001) that on T1-weighted images without magnetization transfer. According to the results of the ROC analysis, sensitivity in detecting  metastases (n = 90) in the brain on T1-SE-MTC and T1-SE was  91.7% and 81.6%, specificity was 100% and 97.6%, respectively.  The accuracy of the T1-SE-MTC is 10% higher in comparison with  the technique without magnetization transfer. Significant differences (p < 0.01) between the size of the foci detected in post-contrast T1- weighted images with magnetization transfer and in post-contrast  T1-weighted images without magnetization transfer, in particular for  foci ≤5 mm in size, were found. Conclusions1. Comparative analysis of CBR showed significant (p < 0.001)  increase of contrast between metastatic lesion and white matter on  T1-SE-MTC in comparison with T1-SE.2. The sensitivity, specificity and accuracy of the magnetization transfer program (T1-SE-MTC) in detecting foci of  metastatic lesions in the brain is significantly higher (p < 0.01), relative to T1-SE.3. The T1-SE-MTC program allows detecting more foci in comparison with T1-SE, in particular foci of ≤5 mm (96% and 86%, respectively, with p < 0.05).

KEBANGGAAN TERHADAP BATIK MADURA BAGI WARGA SUMENEP

2014 ◽  
Vol 4 (2) ◽  
Author(s):  
Sayyida Sayyida ◽  
Nurdody Zakki
Keyword(s):  
Logistic Regression ◽  
Rural Areas ◽  
Research Method ◽  
Community Group ◽  
Parametric Regression ◽  
Logit Function ◽  
The Village ◽  
The City ◽  
Level Of Risk ◽  
Non Parametric

Diversity of Indonesian Batik hanging area. One of the very well-known Indonesian batik is Batik Madura. Batik Madura has become a pride for Indonesia, especially for Madura. The purpose of the study is to model the Sumenep pride to Batik Madura and to see the level of risk or tendency of batik madura pride for the community group Sumenep. This research method uses a non parametric regression used a non-parametric regression because the dependent variable in this study is the variable Y are variables not normally distributed. The results of this study states that the level of risk of the village in Sumenep proud of batik is almost 5 times higher than the islands while people in this city who live in the district town at risk Sumenep proud of Batik Madura 8-fold compared to the archipelago. So it can be concluded that the city is much more proud of batik than those who reside in rural areas especially those who reside in the islands. This study uses data from 100 questionnaires were analyzed using logistic regression analysis. The conclusion of this study is the pride of the batik model as follows: Function logistic regression / logit function: g (x) = 0,074 + 1,568X4(1)+2,159X4(2 this is case the islands as a comparison, X4(1)  is the place to stay in the village and X4(2)  is the place to stay in town, so the Model Opportunities p(x) = EXP(g(x))/1+EXP(g(x)).  Hopes for further research is to conduct research on the development of batik in an integrated region, the need to be disseminated to potential areas of particular potential in Madura batik, especially for residents who reside in the Islands.Keywords: Pride, Batik, Sumenep.

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