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Proceedings Paper

Effect of variable gain on computerized texture analysis on digitized mammograms
Author(s): Hui Li; Maryellen L. Giger; Li Lan; Yading Yuan; Neha Bhooshan; Olufunmilayo I. Olopade
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Paper Abstract

Computerized texture analysis of mammographic images has emerged as a means to characterize breast parenchyma and estimate breast percentage density, and thus, to ultimately assess the risk of developing breast cancer. However, during the digitization process, mammographic images may be modified and optimized for viewing purposes, or mammograms may be digitized with different scanners. It is important to demonstrate how computerized texture analysis will be affected by differences in the digital image acquisition. In this study, mammograms from 172 subjects, 30 women with the BRCA1/2 gene-mutation and 142 low-risk women, were retrospectively collected and digitized. Contrast enhancement based on a look-up table that simulates the histogram of a mixed-density breast was applied on very dense and very fatty breasts. Computerized texture analysis was performed on these transformed images, and the effect of variable gain on computerized texture analysis on mammograms was investigated. Area under the receiver operating characteristic curve (AUC) was used as a figure of merit to assess the individual texture feature performance in the task of distinguishing between the high-risk and the low-risk women for developing breast cancer. For those features based on coarseness measures and fractal measures, the histogram transformation (contrast enhancement) showed little effect on the classification performance of these features. However, as expected, for those features based on gray-scale histogram analysis, such as balance and skewnesss, and contrast measures, large variations were observed in terms of AUC values for those features. Understanding this effect will allow us to better assess breast cancer risk using computerized texture analysis.

Paper Details

Date Published: 9 March 2010
PDF: 6 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76242C (9 March 2010); doi: 10.1117/12.845321
Show Author Affiliations
Hui Li, The Univ. of Chicago (United States)
Maryellen L. Giger, The Univ. of Chicago (United States)
Li Lan, The Univ. of Chicago (United States)
Yading Yuan, The Univ. of Chicago (United States)
Neha Bhooshan, The Univ. of Chicago (United States)
Olufunmilayo I. Olopade, The Univ. of Chicago (United States)

Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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