Ultrasound and other medical imaging technologies remove noise from scanned images by combining the scanned images in an averaging process that reduces and/or eliminates noise inherent in an imaging session. An artifact of the averaging process is blurring effects introduced to the resulting image which diminish the chances of a correct diagnosis because it makes it impossible to identify distinguishable edges that would be associated with an object of interest within the medical image and necessary for diagnosis.
Dr. Rick Mammone and Dr. Christine Podilchuk, researchers at Rutgers University, have developed a novel method that uses deep learning neural networks to detect and correct image blur in scanned images.
This technology deblurs captured medical images generated, stored on, or captured by medical image devices or image displays (e.g., captured by a mobile phone camera.)
In addition, the technology can be bundled with four other related technologies from Dr. Mammone and Dr. Podilchuk to create a complete, cost-effective, and accurate solution for transmitting and interpreting medical images. The solution can capture an image (e.g., user takes a photo via smart phone or another camera) of a displayed medical image. The captured image is processed locally on the device or is transferred for subsequent processing. The other technologies include:
- Easy identification of areas of concern
- Support for images generated by, stored, or captured by imaging devices and a unique ability to capture and enhance medical images on a mobile phone
- Device and software independent
- For diagnosis of a wide array of medical conditions using medical images
- Telemedicine / Teleradiology
- Mobile medical scanning (e.g., mobile ultrasound)
- Medical Image Solutions, Systems & Software
- Electronic Medical/Health Systems (EMR/EHR)
Intellectual Property & Development Status:
US Patent 10,290,084; Available for use with new or existing image display/diagnostic applications or within a complete system comprised of additional related technologies from the inventors. We are seeking licensing and/or industry partners.