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Proceedings Paper

Identifying the optimal segmentors for mass classification in mammograms
Author(s): Yu Zhang; Noriko Tomuro; Jacob Furst; Daniela Stan Raicu
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Paper Abstract

In this paper, we present the results of our investigation on identifying the optimal segmentor(s) from an ensemble of weak segmentors, used in a Computer-Aided Diagnosis (CADx) system which classifies suspicious masses in mammograms as benign or malignant. This is an extension of our previous work, where we used various parameter settings of image enhancement techniques to each suspicious mass (region of interest (ROI)) to obtain several enhanced images, then applied segmentation to each image to obtain several contours of a given mass. Each segmentation in this ensemble is essentially a “weak segmentor” because no single segmentation can produce the optimal result for all images. Then after shape features are computed from the segmented contours, the final classification model was built using logistic regression. The work in this paper focuses on identifying the optimal segmentor(s) from an ensemble mix of weak segmentors. For our purpose, optimal segmentors are those in the ensemble mix which contribute the most to the overall classification rather than the ones that produced high precision segmentation. To measure the segmentors' contribution, we examined weights on the features in the derived logistic regression model and computed the average feature weight for each segmentor. The result showed that, while in general the segmentors with higher segmentation success rates had higher feature weights, some segmentors with lower segmentation rates had high classification feature weights as well.

Paper Details

Date Published: 20 March 2015
PDF: 8 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941342 (20 March 2015); doi: 10.1117/12.2082351
Show Author Affiliations
Yu Zhang, East-West Univ. (United States)
Noriko Tomuro, DePaul Univ. (United States)
Jacob Furst, DePaul Univ. (United States)
Daniela Stan Raicu, DePaul Univ. (United States)

Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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