Seoul National University

Snu Rdb Foundation

Claripi Inc.

Jong Hyo Kim

Hyun Sook Park

Youngwoo Kim

Chul Kyun Ahn

Tai Chul Park

Provided is a deep learning based contrast-enhanced (CE) CT image contrast amplifying method and the deep learning based CE CT image contrast amplifying method includes extracting at least one component CT image between a CE component and a non-CE component for an input CE CT image with the input CE CT image as an input to a previously trained deep learning model; and outputting a contrast-amplified CT image with respect to the CE CT image based on the input CE CT image and the at least one extracted component CT image.

Apparatus and method for contrast amplification of contrast-enhanced CT images based on deep learning

Provided is a method for CT image denoising based on deep learning, and the method for CT image denoising based on deep learning includes: extracting examination information from an input CT image; selecting at least one deep learning model corresponding to the examination information from multiple previously trained deep learning models; and outputting a CT image denoised from the input CT image by feeding the input CT image into the selected at least one deep learning model.

Apparatus and method for medical image denoising based on deep learning

Provided is a denoising method including: generating a synthetic sinogram from an input original CT image; acquiring a noise component from the generated synthetic sinogram; generating a noise component CT image based on the noise component; and reducing noise in the original CT image based on the noise component CT image.

Apparatus and method for denoising CT images

Disclosed is an automated mammographic density estimation method using statistical image information, the method including a preprocessing step of reading the mammogram, segmenting a breast area and shifting pixel values; a step of constructing a tissue probability map in which population-based probability information is extracted, and a probability map for glandular and adipose tissues is constructed; and a density estimation step in which a breast area is segmented based on the constructed tissue probability map and a mammographic density is calculated.

Automated mammographic density estimation and display method using prior probability information, system for the same, and media storing computer program for the same

There is provided a method of reducing noise in a medical image while maintaining structure characteristics within the medical image. The method of reducing noise in the medical image includes: a step of estimating the amount of noise at each pixel by using a look-up table that previously stores physical properties of an image capturing device; a step of calculating a structure direction and a signal coherence at each pixel; a step of performing anisotropic smoothing by using an anisotropic smoothing filter kernel that reflects the structure direction and the signal coherence; and a step of obtaining a structure direction and a signal coherence from an image on which the anisotropic smoothing is performed, obtaining an anisotropic smoothing filter kernel that reflects the obtained structure direction and the signal coherence, and performing filtering that reflects statistical properties of the estimated amount of noise.

Growing community of inventors

Seoul, South Korea

Jong Hyo Kim