scholarly journals A Spectral Filter Array Camera for Clinical Monitoring and Diagnosis: Proof of Concept for Skin Oxygenation Imaging

2019 ◽  
Vol 5 (8) ◽  
pp. 66 ◽  
Author(s):  
Jacob Renzo Bauer ◽  
Arnoud A. Bruins ◽  
Jon Yngve Hardeberg ◽  
Rudolf M. Verdaasdonk
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Optical Filters ◽  
Image Sensor ◽  
Clinical Applications ◽  
Proof Of Concept ◽  
Spectral Filter ◽  
Burn Wound ◽  
Processing Pipeline ◽  
Filter Array

The emerging technology of spectral filter array (SFA) cameras has great potential for clinical applications, due to its unique capability for real time spectral imaging, at a reasonable cost. This makes such cameras particularly suitable for quantification of dynamic processes such as skin oxygenation. Skin oxygenation measurements are useful for burn wound healing assessment and as an indicator of patient complications in the operating room. Due to their unique design, in which all pixels of the image sensor are equipped with different optical filters, SFA cameras require specific image processing steps to obtain meaningful high quality spectral image data. These steps include spatial rearrangement, SFA interpolations and spectral correction. In this paper the feasibility of a commercially available SFA camera for clinical applications is tested. A suitable general image processing pipeline is proposed. As a ’proof of concept’ a complete system for spatial dynamic skin oxygenation measurements is developed and evaluated. In a study including 58 volunteers, oxygenation changes during upper arm occlusion were measured with the proposed SFA system and compared with a validated clinical device for localized oxygenation measurements. The comparison of the clinical standard measurements and SFA results show a good correlation for the relative oxygenation changes. This proposed processing pipeline for SFA cameras shows to be effective for relative oxygenation change imaging. It can be implemented in real time and developed further for absolute spatial oxygenation measurements.

Image Processing for Optical Methods to Analyze Shape, Deformation, Stress and Strain

2006 ◽  
Vol 326-328 ◽  
pp. 13-18
Author(s):  
Yoshiharu Morimoto
Keyword(s):  
Image Processing ◽  
Real Time ◽  
High Speed ◽  
Video Camera ◽  
Image Sensor ◽  
Phase Shifting ◽  
Optical Methods ◽  
Shape Deformation ◽  
Stress And Strain ◽  
Deformation Stress

The authors have been developing some novel methods to measure shape, deformation, stress and strain of structures using optical methods and image processing as follows: (1) Phase analysis methods using Fourier, wavelet or Gabor transforms, etc., (2) Real-time 2-D strain measurement using moiré interferometry, (3) Scanning moiré method using thinning-out of scanning lines and a DMD camera (4) Strain rate distribution measurement by a high-speed video camera, (5) Real-time integrated phase-shifting method, (6) Shape measurement methods using multi-reference planes, a linear image sensor, or a frequency modulated grating, and (7) Windowed phase-shifting digital holographic interferometry (WPSDHI). Theories of these methods and some applications are introduced. The most accurate result is 88 picometer standard deviation of errors using the WPSDHI.

scholarly journals A library for fMRI real-time processing systems in python (RTPSpy) with comprehensive online noise reduction, fast and accurate anatomical image processing, and online processing simulation

2021 ◽  
Author(s):  
Masaya Misaki ◽  
Jerzy Bodurka ◽  
Martin P Paulus
Keyword(s):  
Image Processing ◽  
Data Processing ◽  
Real Time ◽  
Easy Access ◽  
External Application ◽  
Real Time Processing ◽  
Time Processing ◽  
Target Signal ◽  
Processing Pipeline ◽  
Anatomical Image

We introduce a python library for real-time fMRI (rtfMRI) data processing systems, Real-Time Processing System in python (RTPSpy), to provide building blocks for a custom rtfMRI application with extensive and advanced functionalities. RTPSpy is a library package including 1) a fast, comprehensive, and flexible online fMRI denoising pipeline comparable to offline processing, 2) utilities for fast and accurate anatomical image processing to define a target region on-site, 3) a simulation system of online fMRI processing to optimize a pipeline and target signal calculation, 4) interface to an external application for feedback presentation, and 5) a boilerplate graphical user interface (GUI) integrating operations with RTPSpy library. Since online fMRI data processing cannot be equivalent to offline, we discussed the limitations of online analysis and their solutions in the RTPSpy implementation. We developed a fast and accurate anatomical image processing script with fast tissue segmentation (FastSeg), image alignment, and spatial normalization, utilizing the FastSurfer, AFNI, and ANTs. We confirmed that the FastSeg output was comparable with FreeSurfer, and could complete all the anatomical image processing in a few minutes. Thanks to its highly modular architecture, RTPSpy can easily be used for a simulation analysis to optimize a processing pipeline and target signal calculation. We present a sample script for building a real-time processing pipeline and running a simulation using RTPSpy. The library also offers a simple signal exchange mechanism with an external application. An external application can receive a real-time neurofeedback signal from RTPSpy in a background thread with a few lines of script. While the main components of the RTPSpy are the library modules, we also provide a GUI class for easy access to the RTPSpy functions. The boilerplate GUI application provided with the package allows users to develop a customized rtfMRI application with minimum scripting labor. Finally, we discussed the limitations of the package regarding environment-specific implementations. We believe that RTPSpy is an attractive option for developing rtfMRI applications highly optimized for individual purposes. The package is available from GitHub (https://github.com/mamisaki/RTPSpy) with GPL3 license.

scholarly journals High-Level Synthesis of Online K-Means Clustering Hardware for a Real-Time Image Processing Pipeline

2019 ◽  
Vol 5 (3) ◽  
pp. 38 ◽  
Author(s):  
Aiman Badawi ◽  
Muhammad Bilal
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Real Time ◽  
Moving Average ◽  
Weighted Average ◽  
High Level Synthesis ◽  
Hardware Accelerator ◽  
Hardware Complexity ◽  
Processing Pipeline ◽  
High Level

The growing need for smart surveillance solutions requires that modern video capturing devices to be equipped with advance features, such as object detection, scene characterization, and event detection, etc. Image segmentation into various connected regions is a vital pre-processing step in these and other advanced computer vision algorithms. Thus, the inclusion of a hardware accelerator for this task in the conventional image processing pipeline inevitably reduces the workload for more advanced operations downstream. Moreover, design entry by using high-level synthesis tools is gaining popularity for the facilitation of system development under a rapid prototyping paradigm. To address these design requirements, we have developed a hardware accelerator for image segmentation, based on an online K-Means algorithm using a Simulink high-level synthesis tool. The developed hardware uses a standard pixel streaming protocol, and it can be readily inserted into any image processing pipeline as an Intellectual Property (IP) core on a Field Programmable Gate Array (FPGA). Furthermore, the proposed design reduces the hardware complexity of the conventional architectures by employing a weighted instead of a moving average to update the clusters. Experimental evidence has also been provided to demonstrate that the proposed weighted average-based approach yields better results than the conventional moving average on test video sequences. The synthesized hardware has been tested in real-time environment to process Full HD video at 26.5 fps, while the estimated dynamic power consumption is less than 90 mW on the Xilinx Zynq-7000 SOC.

A scalable, real-time, image processing pipeline

1995 ◽  
Vol 8 (2) ◽  
pp. 110-121 ◽  
Author(s):  
Pieter P. Jonker ◽  
Erwin R. Komen ◽  
Martin A. Kraaijveld
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Processing Pipeline ◽  
Real Time Image Processing ◽  
Real Time Image ◽  
Time Image

scholarly journals Circuitry and Proof of Concept for an Adaptive Traffic Control System

2021 ◽  
Vol 9 (VI) ◽  
pp. 4063-4067
Author(s):  
M Vaishnavi
Keyword(s):  
Image Processing ◽  
Control System ◽  
Open Source ◽  
Real Time ◽  
Traffic Control ◽  
Proof Of Concept ◽  
The Road ◽  
Traffic Control System ◽  
On The Road ◽  
Adaptive Traffic Control

This paper illustrates the circuitry and proof of concept of a novel density based traffic mitigating system for the vehicles. The intention of this paper is to make an adaptive signalling system, which can be optimally used in real-time. This project is accomplished with the help of NVIDIA Jetson Nano and utilizes python for image processing as open source in order to measure the size of the traffic on the road.

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