Magnetic resonance imaging in the initial diagnosis of pancreatic insulinoma: primary data analysis

2019 ◽  
Author(s):  
Marina Yukina ◽  
Nurana Nuralieva ◽  
Ekaterina Troshina ◽  
Aleksandr Vorontsov ◽  
Victoria Vladimirova ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Data Analysis ◽  
Magnetic Resonance ◽  
Initial Diagnosis ◽  
Primary Data ◽  
Resonance Imaging ◽  
Primary Data Analysis ◽  
Pancreatic Insulinoma

Statistical Analysis of Functional Magnetic Resonance Imaging Data

2017 ◽  
pp. 103-114
Author(s):  
Nicole A. Lazar
Keyword(s):  
Magnetic Resonance Imaging ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Temporal Correlation ◽  
Fmri Data ◽  
Imaging Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging

The analysis of functional magnetic resonance imaging (fMRI) data poses many statistical challenges. The data are massive, noisy, and have a complicated spatial and temporal correlation structure. This chapter introduces the basics of fMRI data collection and surveys common approaches for data analysis.

Advanced data analysis in brain functional studies with magnetic resonance imaging

2006 ◽  
Vol 60 (8) ◽  
pp. 477
Author(s):  
N. Vanello ◽  
M.F. Santarelli ◽  
V. Positano ◽  
E. Ricciardi ◽  
P. Pietrini ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Data Analysis ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Functional Studies

scholarly journals The Relation between Color Spaces and Compositional Data Analysis Demonstrated with Magnetic Resonance Image Processing Applications

2018 ◽  
Vol 47 (5) ◽  
pp. 34-46
Author(s):  
Omer Faruk Gulban
Keyword(s):  
Magnetic Resonance Imaging ◽  
Image Processing ◽  
Data Analysis ◽  
Magnetic Resonance ◽  
Compositional Data ◽  
Color Image ◽  
Color Space ◽  
Color Image Processing ◽  
Compositional Data Analysis ◽  
Resonance Imaging

This paper presents a novel application of compositional data analysis methods in the context of color image processing. A vector decomposition method is proposed to reveal compositional components of any vector with positive components followed by compositional data analysis to demonstrate the relation between color space concepts such as hue and saturation to their compositional counterparts. The proposed methods are applied to a magnetic resonance imaging dataset acquired from a living human brain and a digital color photograph to perform image fusion. Potential future applications in magnetic resonance imaging are mentioned and the benefits/disadvantages of the proposed methods are discussed in terms of color image processing.

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