Technologies

Background

Noninvasive, digital imaging is a multi-billion dollar industry with many applications and is ubiquitous to our culture. It is used in the medical field as a diagnostic tool, in ultrasound, magnetic resonance imaging (MRI) and retinal image characterization. However, digital imaging is also prevalent in such diverse areas as homeland security, nanoscience research, and the film industry to name but a few. In most contexts and applications, though, image quality defines the value of the tool. The better the picture or image quality, the more valuable the tool. However, with digital images and videos, comes the possibility of extracting more information than just an image. In mammography, for example, an image might reveal an abnormality yet not be able to “tell” the physician whether it is cancer or not. In such a case, further testing is required to determine the presence or absence of cancer. A method to reprocess the image or video to increase the selectivity would be invaluable to more accurate screening for cancer and other situations with specific morphologic or dynamic characteristics. Such a method would have wider implications in enabling not only better cancer screening, but also target identification, motion tracking, activity identification, and possibly drug and explosives detection.

Technology Description

A method has been developed to represent image content for image analysis purposes. The method includes efficient and robust Amplitude-Modulation Frequency-Modulation (AM-FM) algorithms for representing 2D and 3D signals. It also includes image and video classification algorithms based on AM-FM features, such as 2D/3D signal analysis, 2D/3D reconstructions, 2D/3D signal classification, motion estimation and activity recognition in videos. Additionally, the method includes an efficient formulation of a variable spacing, local linear phase method (VS-LLP) for instantaneous frequency (IF) estimation. Said method has demonstrated preliminary success in the potential to identify retinal abnormalities, predict plaque rupture in atherosclerosis, texture analysis in medical images, and discriminate between peaceful and violent human activity.

Publications

• Multiscale directional AM-FM demodulation of images using a 2D optimized method
• Multiscale AM-FM Demodulation and Image Reconstruction Methods With Improved Accuracy
• Recent multiscale AM-FM methods in emerging applications in medical imaging
• Robust Multiscale AM-FM Demodulation of Digital Images

About STC.UNM

As the technology-transfer and economic-development organization for the University of New Mexico, STC.UNM protects and commercializes technologies developed at the University of New Mexico (UNM) by filing patents and copyrights and transferring the technologies to the marketplace. We connect the business communication (companies, entrepreneurs and investors) to these UNM technologies for licensing opportunities and the creation of startup companies.