scholarly journals Improvements to strip-based digital image registration for robust eye-tracking and to minimize distortions in images from scanned ophthalmic imaging systems

2021 ◽  
Author(s):  
Min Zhang ◽  
Elena Gofas-Salas ◽  
BIANCA LEONARD ◽  
Yuhua Rui ◽  
Valerie Snyder ◽  
...  
Keyword(s):  
Image Registration ◽  
Eye Tracking ◽  
Digital Image ◽  
Imaging Systems ◽  
Ophthalmic Imaging

scholarly journals Improvements to strip-based digital image registration for robust eye-tracking and to minimize distortions in images from scanned ophthalmic imaging systems

2020 ◽  
Author(s):  
Min Zhang ◽  
Elena Gofas-Salas ◽  
Bianca T. Leonard ◽  
Yuhua Rui ◽  
Valerie Snyder ◽  
...  
Keyword(s):  
Image Registration ◽  
Eye Tracking ◽  
Real Time ◽  
Imaging Systems ◽  
Scanning Laser ◽  
Scanning Laser Ophthalmoscopy ◽  
Real Time Processing ◽  
Time Processing ◽  
Ophthalmic Imaging ◽  
Eye Motion

ABSTRACTRetinal image-based eye tracking from scanned ophthalmic imaging systems, such as scanning laser ophthalmoscopy, has allowed for precise real-time eye tracking at sub-micron resolution. To achieve real-time processing rates, strip-based image registration methods for real-time applications have several constraints that limit their performance. This trade-off is acceptable for many imaging and psychophysical applications but when the objective is precise eye motion measurement over time, a high error tolerance can be consequential. Dropped strips in these applications can complicate FEMs quantification. Some light starved imaging applications, such as autofluorescence retinal imaging, also require the retention and registration of as much of the data as possible to increase the signal to noise ratio in the final integrated or averaged image. We show here that eye motion can be extracted from image sequences from scanned imaging systems more consistently when the constraints of real-time processing are lifted, and all data is available at the time of registration. This is enabled with additional image processing steps to achieve a more robust solution. Our iterative approach identifies and discards distorted frames, detects coarse motion to generate a synthetic reference frame and then uses it for fine scale motion tracking with improved sensitivity over a larger area. We demonstrate its application here to tracking scanning laser ophthalmoscopy (TSLO) and adaptive optics scanning light ophthalmoscopy (AOSLO). We show that it can successfully capture most of the eye motion across each image sequence, leaving only between 0.04-3.39% of non-blink frames untracked, even with low quality images, while simultaneously minimizing image distortions induced from eye motion. These improvements will facilitate precise FEMs measurement in TSLO and longitudinal tracking of individual cells in AOSLO.

Development of New Image Registration Techniques – A Research Framework

2018 ◽  
Vol 8 (3) ◽  
pp. 50
Author(s):  
Sindhu Madhuri G. ◽  
Indira Gandhi M P
Keyword(s):  
Image Processing ◽  
Image Registration ◽  
Digital Image Processing ◽  
Digital Image ◽  
Creative Thinking ◽  
Reference Image ◽  
Transformation Technique ◽  
Research Framework ◽  
Registration Process ◽  
Data Source

Image is a basic and fundamental data source for the digital image processing. This image data source is required to be processed into information or intelligence and further to knowledge levels where it is required to understand and migrate into knowledge economy systems. Image registration is one of such key and most important process already identified in the digital image processing domain. Image registration is a process of bringing the reference image and sensed image into a common co-ordinate system, and application of complex transformation techniques for necessary comparison of reference with sensed images obtained from different - views, times, spaces, etc., in order to extract the valuable information and intelligence embedded in them. Due to the complexity of overall image registration process, it is difficult to suggest a single transformation technique even for a specific application. In addition, it is highly impossible to suggest one single transformation technique for comparison of various sensed images with a reference image during the image registration process. This research gap calls for the development of new image registration techniques for the application of more than one transformation technique during the image registration process for the necessary comparisons with reference image & sensed images, those are obtained from the available heterogeneous sources or sensors, based on the requirement. In addition, it is a basic need to attempt for the measurement of effectiveness of the image registration process also. Therefore, a research framework is developed for image registration process and attempted for the measurement of its effectiveness also. This new research area is a novel idea, and is expected to emerge as a provision for the knowledge computations with creative thinking through the embedded intelligence extraction during the complex image registration process.

scholarly journals Satellite Image Registration and Image Fusion by using Principle Component Analysis

2018 ◽  
Vol 7 (2.19) ◽  
pp. 106
Author(s):  
Gandla Maharnisha ◽  
Gandla Roopesh Kumar ◽  
R Arunraj
Keyword(s):  
Image Processing ◽  
Image Registration ◽  
Image Fusion ◽  
Digital Image Processing ◽  
Spatial Resolution ◽  
Digital Image ◽  
Principle Component Analysis ◽  
Component Analysis ◽  
Principle Component ◽  
Spectral Degradation

This aims to fused image registration and image fusion used to spatial resolution images by principle component analysis method. Digital image processing requires either the full image or a part of image. It will be processed from the user’s point of view like the radius of object. Wavelet technique will improve the spatial resolution to produce spectral degradation in output image. To overcome the spectral degradation, PCA fusion method can be used. PCA uses curve which represent edges and extraction of the detailed information from the image.PAN and MS images are used by individual acquired low frequency approximate component and high frequency detail components in this PCA. To evaluate the image fusion accuracy, Peak Signal to Noise Ratio and Root Mean Square Error are used. The advantages of using digital image processing are preservation of original data accuracy, flexibility and repeatability. 

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