| Abstract |
The main goal of this work is the development of techniques for higher-level image analysis, i.e., object-level analysis, of breast cancer imagery. Established cytologic (cell) criteria can be contradictory, and even histologic (tissue) criteria (considered the gold standard for diagnosis) are subject to varied interpretation. There is thus a need to quantitatively define characteristics of breast cancer to better coordinate clinical care of women presenting breast masses.
The main contributions in this work are four-fold. First, we quantitatively analyze the utility of multispectral imagery (29 bands) for classification and segmentation tasks in histopathology imagery. Second, we develop object-level segmentations for several histologic classes, as well as a quantitative object-level segmentation metric. Third, we extract a comprehensive set of both object- and spatial-level features which are used in a feature selection framework for classification of objects and imagery. Fourth, we extend the concepts of object-level features to higher-level image objects, analyze the utility of these high-level objects for image classification.
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| Bio |
Laura E. Boucheron received a Bachelor of Science in electrical engineering from New Mexico State University in 2001 and a Master of Science in electrical engineering from New Mexico State University in 2003. She received a Ph.D. in electrical and computer engineering from the University of California, Santa Barbara, CA, USA in 2008.
She is currently a Postdoctoral Fellow at New Mexico State University in the Klipsch School of Electrical and Computer Engineering. She has previous intern and graduate research assistant experience at both Sandia National Laboratories and Los Alamos National Laboratory. Her main research interests include high-level image analysis and pattern classification, with special interest in pathology imagery (histo- and cyto-pathology).
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