Rutgers, the State University of New Jersey

University of Pennsylvania

Boston University

Elucid Bioimaging Inc.

Shannon C Agner

Anant Madabhushi

Mark Rosen

Andrew J Buckler

James Hamilton

The described invention provides systems and methods for detecting and segmenting a lesion from longitudinal, time series, or multi-parametric imaging by utilizing spectral embedding-based active contour (SEAC). In addition, the described invention further provides systems and methods for registering time series data by utilizing reduced-dimension eigenvectors derived from spectral embedding (SE) of feature scenes (SERg).

Method and apparatus for segmentation and registration of longitudinal images

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

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Shannon C Agner