Study on Pipelined Parallel Processing Architectures for Imaging and Computer Vision

2021 ◽  
pp. 75-95
Author(s):  
P. Suresh
Keyword(s):  
Magnetic Resonance Imaging ◽  
Image Processing ◽  
Computer Vision ◽  
Magnetic Resonance ◽  
Geographical Information Systems ◽  
Daily Life ◽  
Industrial Applications ◽  
Geographical Information ◽  
Satellite Television ◽  
Processing Architectures

In digital image processing, the noise detection and removal are very important tasks, since they have wide applications in all fields. In recent years, the logic fabric and routing FPGAs architecture provides customers with a number of advantages. In this chapter, the performance is analysed with different FPGA processors in terms of slices, LUTs, and BRAM utilization is studied. The implemented hardware architecture with digital images plays an important role in all daily life applications, industrial applications such as image recognition, computer tomography, satellite television, space imaging, magnetic resonance imaging, etc., and also in areas of research and technology of geographical information systems and astronomy.

Magnetic Resonance Imaging Multispectral Tissue Classification

Physiology ◽  
1988 ◽  
Vol 3 (4) ◽  
pp. 148-154
Author(s):  
MW Vannier ◽  
CM Speidel ◽  
DL Rickman
Keyword(s):  
Magnetic Resonance Imaging ◽  
Image Processing ◽  
Magnetic Resonance ◽  
Statistical Evaluation ◽  
Multispectral Image ◽  
Processing Technology ◽  
Resonance Imaging ◽  
Analysis Application ◽  
Software And Hardware ◽  
Multispectral Analysis

The application of NASA multispectral image processing technology for analysis of magnetic resonance imaging scans has been studied. Software and hardware capability has been developed, and a statistical evaluation of multispectral analysis application to magnetic resonance imaging scans of the head has been performed.

scholarly journals Automatic Assessment of Human Gastric Motility and Emptying from Dynamic 3D Magnetic Resonance Imaging

2020 ◽  
Author(s):  
Kun-Han Lu ◽  
Zhongming Liu ◽  
Deborah Jaffey ◽  
John Wo ◽  
Kristine Mosier ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Image Processing ◽  
Gastric Emptying ◽  
Magnetic Resonance ◽  
Gastric Motility ◽  
Type I ◽  
Resonance Imaging ◽  
Automatic Assessment ◽  
Processing Pipeline ◽  
Healthy Humans

Background: Time-sequenced magnetic resonance imaging (MRI) of the stomach is an emerging technique for non-invasive assessment of gastric emptying and motility. However, an automated and systematic image processing pipeline for analyzing dynamic 3D (i.e., 4D) gastric MRI data is not yet available. This study introduces an MRI protocol for imaging the stomach with high spatiotemporal isotropic resolution and provides an integrated pipeline for assessing gastric emptying and motility simultaneously. Methods: Diet contrast-enhanced MRI images were acquired from seventeen healthy humans after they consumed a naturalistic contrast meal. An automated image processing pipeline was developed to correct for respiratory motion, to segment and compartmentalize the lumen-enhanced stomach, to quantify total gastric and compartmental emptying, and to compute and visualize gastric motility on the surface of the stomach. Key Results: The gastric segmentation reached an accuracy of 91.10±0.43% with the Type-I error and Type-II error being 0.11±0.01% and 0.22±0.01%, respectively. Gastric volume decreased 34.64±2.8% over 1 hour where the emptying followed a linear-exponential pattern. The gastric motility showed peristaltic patterns with a median = 4 wave-fronts (range 3 - 6) and a mean frequency of 3.09±0.07 cycles per minute (CPM). Further, the contractile amplitude was stronger in the antrum than in the corpus (antrum vs. corpus: 5.18±0.24 vs. 3.30±0.16 mm; p < .001). Conclusions & Inferences: The automated, streamlined software can process dynamic 3D MRI images and produce comprehensive and personalized profiles of gastric motility and emptying. This software will facilitate the application of MRI for monitoring gastric dynamics in research and clinical settings.

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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