Whole Body Organ Segmentation

Progressive Data Transmission for Hierarchical Detection in a Cloud

Hierarchical Detection Network (HDN)

Location Registration and Recognition (LRR)
Given are (a) two temporally separated CT scans, and , and (b) a series of locations in . The goal is to produce, for each location, an affine transformation mapping the locations and their immediate neighborhood from to . The system essentially "recognizes" the neighborhoods at near interactive speeds.

Covariance Driven Correspondences (CDC)
The uncertainty of point correspondences is derived from the covariance matrices of the individual point locations and from the covariance matrix of the estimated transformation parameters. Based on this uncertainty, CDC uses a robust objective function and an EM-like algorithm to simultaneously estimate the transformation.

Retinal Vessel Centerline Extraction
We propose a new technique motivated by the goals of improving detection of low-contrast and narrow vessels and eliminating false detections at non-vascular structures. Novel low level vesselness measure is embedded into a vessel tracing framework, resulting in an efficient and effective vessel centerline extraction algorithm.

Automated registration of challenging image pairs (GDBICP)
Our goal is an automated registration algorithm capable of aligning image pairs having some combination of low overlap, substantial orientation and scale differences, large illumination differences (e.g. day and night), substantial scene changes, and different modalities.

Retinal Image Vessel Extraction and Registration System (RIVERS)
This web based system is as a fully automatic tool for vasculature detection and alignment of retinal images. Our techniques enable superior montaging, and animation of images for easy visualization of disease progression. Modalities supported are red-free, color, and fluorescein angiogram images.