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Proceedings Paper

A robust model order estimation and segmentation technique for classification of biopsies in breast cancer
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Paper Abstract

The difficult problem of identifying dominant structures in unknown data sets has been elegantly addressed recently by a non-parametric information theoretic approach, the "Jump" method. The method employs an appropriate but fixed power transformation on the distortion-rate, D(R), curve estimated by the popular K-means algorithm. Although this approach yields good results asymptotically for higher dimensional spaces, in many practical cases involving lower dimensional spaces, a transformation function with a fixed power may not find the correct model order. The work presented here develops an objective function to derive a more suitable transformation function that minimizes classification error in low dimensional data sets. In addition, a number of carefully chosen K-means seeding methods based upon proper heuristic choices have been used to enhance the detection sensitivity and to allow a more accurate estimation. The proposed method has been evaluated for a large variety of datasets and compared with the original Jump method and other well-known order estimation methods such as Minimum Description Length (MDL), Akaike Information Criteria (AIC), and Consistent Akaike Information Criteria (CAIC), demonstrating superior overall performance. Comparative results for the Wisconsin Diagnostic Breast Cancer Dataset have been included. This modified information theoretic approach to model order estimation is expected to improve and validate diagnostic classification and detection of pre-cancerous lesions. Other applications such as finding plausible number of segments in image segmentation scenarios are also possible.a

Paper Details

Date Published: 13 March 2010
PDF: 9 pages
Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 762321 (13 March 2010); doi: 10.1117/12.845609
Show Author Affiliations
Enrique Corona, Texas Tech Univ. (United States)
Brian Nutter, Texas Tech Univ. (United States)
Sunanda Mitra, Texas Tech Univ. (United States)


Published in SPIE Proceedings Vol. 7623:
Medical Imaging 2010: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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