Collaboration Project with:
Edmond Szabo MD and Murray Resnik MD PhD
Department of Pathology RIH and Brown University
People: Haynes Heaton, H. Can Aras
Motivation/Goal
- Digitally cloning Dr. Resnick
- Determining the level of the cancer from
the biopsy
images using computational techniques
Our goal is to create a completely automated algorithm to determine the level of
malignancy of images of tumor biopsies. We do this by first segmenting the nuclei
out of the image, defining statistics about the "pleomorphism" of the nuclei, and
then using machine learning techniques to categorize the images into normal, low,
and high malignancy. Pleomorphism includes nuclei size, variability of size, complexity
of shape, eccentricity, heterogeneity of color, heterogeneity and organization of
orientation, and many more higher level features that all deal with complexity and
heterogeneity that result from malignant tissue.
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Approach: Segmentation
- watershed segmentation
- use of gradient of the image
- intensity value at each pixel
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