Rutgers, The State University of New Jersey

University of Pennsylvania

Boston University

Elucid Bioimaging Inc.

University of Pa.

Mark Rosen

Anant Madabhushi

Michael D Feldman

Shannon C Agner

Andrew J Buckler

Pallavi Tiwari

Satish E Viswanath

Jianbo Shi

John Tomaszewski

Robert Toth

James Hamilton

John Tomaszeweski

A method and apparatus for classifying possibly vulnerable plaques from sets of DCE-MRI images includes receiving a set of MRI slice images obtained at respectively different times, where each slice image includes voxels representative of at least one region of interest (ROI). The images are processed to determine the boundaries of the ROIs and the voxels within the identified boundaries in corresponding regions of the images from each time period are processed to extract kinetic texture features. The kinetic texture features are then used in a classification process which classifies the ROIs as vulnerable or stable.

Classification of biological tissue by multi-mode data registration, segmentation and characterization

A method and apparatus for classifying possibly malignant lesions from sets of DCE-MRI images includes receiving a set of MRI slice images obtained at respectively different times, where each slice image includes voxels representative of at least one region of interest (ROI). The images are processed to determine the boundaries of the ROIs and the voxels within the identified boundaries in corresponding regions of the images from each time period are processed to extract kinetic texture features. The kinetic texture features are then used in a classification process which classifies the ROIs as malignant or benign. The malignant lesions are further classified to separate TN lesions from non-TN lesions.

System and method for automated segmentation, characterization, and classification of possibly malignant lesions and stratification of malignant tumors

This invention relates to computer-assisted diagnostics and classification of prostate cancer. Specifically, the invention relates to segmentation of the prostate boundary on MRI images, cancer detection using multimodal multi-protocol MR data; and their integration for a computer-aided diagnosis and classification system for prostate cancer.

Computer assisted diagnosis (CAD) of cancer using multi-functional, multi-modal in-vivo magnetic resonance spectroscopy (MRS) and imaging (MRI)

This invention relates to supervised or unsupervised classification of biological datasets. Specifically, the invention relates to the use of Graph Embedding as a method of reducing dimensionality thereby improving supervised classification of classes, both conventional and new ones.

Systems and methods for classification of biological datasets

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Bala Cynwyd, PA, United States of America

Mark Rosen