Case Western Reserve University

Robert Gilkeson

Anant Madabhushi

Nathaniel Braman

Mirabela Rusu

Mahdi Orooji

Philip Linden

David L Wilson

Mehdi Alilou

Di Wen

Baowei Fei

David Wilson

Methods and apparatus for grading coronary calcium, including an example apparatus that includes a set of logics and a memory that stores a set of radiographic images. The set of logics includes an image acquisition logic that acquires a set of radiographic images of a region of tissue, the set of radiographic images including a low-energy reference x-ray image and a higher-energy floating x-ray image, a registration logic that produces a set of registered images from a plurality of members of the set of radiographic images, an enhancement logic that produces a set of calcium-enhanced images from the set of registered images, a post-enhancement logic that produces a bone image from the set of calcium-enhanced images, and a grading logic that computes a coronary heart disease prediction score based, at least in part, on the bone image and a set of features extracted from the bone image.

Dual energy x-ray coronary calcium grading

Embodiments associated with classifying a region of tissue using features extracted from nodules and surrounding structures. One example apparatus includes a feature extraction circuit configured to automatically extract a first set of quantitative features from a nodule represented in at least one CT image, and automatically extract a second set of quantitative features from the lung parenchyma region immediately surrounding the nodule represented in the at least one CT image; a feature selection circuit configured to select an optimally predictive feature set from the first set of quantitative features and the second set of quantitative features; and a training circuit configured to train a classifier using the optimally predictive feature set to assign malignancy risk to a lung nodule represented in a CT image of a region of tissue demonstrating lung nodules. A prognosis or treatment plan may be provided based on the malignancy risk.

Characterizing lung nodule risk with quantitative nodule and perinodular radiomics

Methods, apparatus, and other embodiments associated with classifying a region of tissue using textural analysis are described. One example apparatus includes an image acquisition logic that acquires an image of a region of tissue demonstrating cancerous pathology, a delineation logic that distinguishes nodule tissue within the image from the background of the image, a perinodular zone logic that defines a perinodular zone based on the nodule, a feature extraction logic that extracts a set of features from the image, a probability logic that computes a probability that the nodule is benign or that the nodule will respond to a treatment, and a classification logic that classifies the nodule tissue based, at least in part, on the set of features or the probability. A prognosis or treatment plan may be provided based on the classification of the image.

Decision support for disease characterization and treatment response with disease and peri-disease radiomics

Methods, apparatus, and other embodiments associated with classifying a region of tissue using quantified vessel tortuosity are described. One example apparatus includes an image acquisition logic that acquires an image of a region of tissue demonstrating cancerous pathology, a delineation logic that distinguishes nodule tissue within the image from the background of the image, a perinodular zone logic that defines a perinodular zone based on the nodule, a feature extraction logic that extracts a set of features from the image including a set of tortuosity features, a probability logic that computes a probability that the nodule is benign, and a classification logic that classifies the nodule tissue based, at least in part, on the set of features or the probability. A prognosis or treatment plan may be provided based on the classification of the image.

Characterizing disease and treatment response with quantitative vessel tortuosity radiomics

A method is provided that comprises receiving a first digital radiography (DR) image of at least a portion of a body of a patient, receiving a second DR image of the at least a portion of a body of a patient, the first DR image being captured at a different energy level than the second DR image, and determining common control point locations for both the first and second DR images. The method further comprises generating an optimized DR image by moving portions of a selected one of the first and second DR images with its associated control points to locations that correspond to similar portions of the other of the first and second DR images, applying deformable transformation to one of the first and second DR images and performing a log subtraction on the first and second DR image to generate a dual-energy digital radiography (DEDR) image.

Systems and methods for generating images for identifying diseases

Systems and methods for generating images for identifying diseases are provided. In one embodiment, a method comprises receiving a first digital radiography (DR) image of at least a portion of a body of a patient, receiving a second DR image of the at least a portion of a body of a patient, the first DR image being captured at a different energy level than the second DR image, and determining common control point locations for both the first and second DR images. The method further comprises generating an optimized DR image by moving portions of a selected one of the first and second DR images with its associated control points to locations that correspond to similar portions of the other of the first and second DR images, applying deformable transformation to one of the first and second DR images and performing a log subtraction on the first and second DR image to generate a dual-energy digital radiography (DEDR) image.

Growing community of inventors

Cleveland Heights, OH, United States of America

Robert Gilkeson