Method for Automatically Generating a Large Field of View Image From Many Small Field of View Images

Brief Description:

Imaging systems that provide microscopic resolution often have a small field of view.  When the sample to be imaged is large, several small field of view images must be obtained and stitched together to form an image of the entire sample.  Current methodologies for doing this are long, tedious and labor intensive.  This problem is particularly marked for adaptive optics scanning laser ophthalmoscopy (ALSLO), whose typical field of view is only ~ 1x1 degree.  Conventional clinical imaging systems acquire data at low resolution of areas 30x30 degrees and up.  For AOSLO imaging to become a clinically relevant tool, high resolution large field of view images will need to be rapidly acquired, compared to those that clinicians currently utilize to make patient care decisions.  This can only be done by automating the process of montaging the small field of view images into a large field of view image.


This technology utilizes custom software designed to rapidly; identify the relative location of each small field of view image, determine the course offset between adjacent images, determine the precise offset between sub-regions within adjacent fields, combine and average overlap regions for adjacent images, and montage all the small field of view images into a high resolution large field of view image.  This dramatically reduces the amount of time and expertise necessary to produce these images.



The direct application of this invention is to automatically generate large field of view images of the human retina from small field of view AOSLO images. The method is not limited to ophthalmoscopic imaging systems, but could be applied to any small field high resolution imaging system (such as scanning electron microscopy) that is used to image a sample that is large relative to the field of view of the instrument.



As compared to current methodologies, which are labor intensive and demand the efforts of a high skilled individual, this technology greatly reduces the amount of time and expertise necessary to produce high resolution large field of view ophthalmic images.  By completely automating the image montaging process, AOSLO imaging could become a clinically relevant tool in ophthalmology.

URV Reference Number: 2-11150-13022
Patent Information:
Computer Software
For Information, Contact:
McKenna Geiger
Licensing Manager
University of Rochester
Ethan Rossi
Qiang Yang