Quantitative Insights, Inc.

Other

Karen Drukker

Maryellen L Giger

Hui Li

Li Lan

Gillian M Newstead

Michael R Chinander

Neha Bhooshan

Jeremy Bancroft Brown

Andrew Robert Jamieson

Robert Tomek

Carl J Vyborny

Karla Horsch

Computerized interpretation of medical images for quantitative analysis of multi-modality breast images including analysis of FFDM, 2D/3D ultrasound, MRI, or other breast imaging methods. Real-time characterization of tumors and background tissue, and calculation of image-based biomarkers is provided for breast cancer detection, diagnosis, prognosis, risk assessment, and therapy response. Analysis includes lesion segmentation, and extraction of relevant characteristics (textural/morphological/kinetic features) from lesion-based or voxel-based analyses. Combinations of characteristics in several classification tasks using artificial intelligence is provided. Output in terms of 1D, 2D or 3D distributions in which an unknown case is identified relative to calculations on known or unlabeled cases, which can go through a dimension-reduction technique. Output to 3D shows relationships of the unknown case to a cloud of known or unlabeled cases, in which the cloud demonstrates the structure of the population of patients with and without the disease.

Method, system, software and medium for advanced intelligent image analysis and display of medical images and information

Computerized interpretation of medical images for quantitative analysis of multi-modality breast images including analysis of FFDM, 2D/3D ultrasound, MRI, or other breast imaging methods. Real-time characterization of tumors and background tissue, and calculation of image-based biomarkers is provided for breast cancer detection, diagnosis, prognosis, risk assessment, and therapy response. Analysis includes lesion segmentation, and extraction of relevant characteristics (textural/morphological/kinetic features) from lesion-based or voxel-based analyzes. Combinations of characteristics in several classification tasks using artificial intelligence is provided. Output in terms of 1D, 2D or 3D distributions in which an unknown case is identified relative to calculations on known or unlabeled cases, which can go through a dimension-reduction technique. Output to 3D shows relationships of the unknown case to a cloud of known or unlabeled cases, in which the cloud demonstrates the structure of the population of patients with and without the disease.

A method of detecting a candidate abnormality in a sonographic medical image, based on determining a radial gradient index (RGI) at plural pixels, producing an RGI image, thresholding the RGI image, determining a candidate abnormality based on the thresholding step, and locating a center point of the candidate abnormality. The candidate abnormality may be classified by segmenting the candidate abnormality, including determining average radial gradients (ARDs) in the sonographic medical image based on the center point, extracting plural features from the segmented candidate abnormality, and determining a likelihood of the candidate abnormality being an actual abnormality based on the extracted plural features.

Automated method and system for the detection of abnormalities in sonographic images

Growing community of inventors

Crete, IL, United States of America

Karen Drukker