Non-Invasive Optical Imaging in vivo for Early Detection and Advanced Diagnosis of Cancer

This Project is no longer active.

We are exploring cutting edge optical imaging technology combined with robotics and computer vision technology and statistical learning algorithms to find the most discriminative image features for cancer detection and diagnosis. This work is carried out by an interdisciplinary team of researchers from both Carnegie Mellon University and University of Pittsburgh, and is supported by the unconventional innovation program of National Cancer Institute.

The research focus in the project is twofold:

develop methodology for image feature extraction from multispectral biological images, find the most discriminating feature subsets, achieve high classification rates with minimum false positive rate, and provide direct feedback to the imaging process.
construct a 3D robotics imaging system using multiple cameras and lighting sources for detection of skin cancer through reconstruction of 4D spatiotemporal images.

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