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Proceedings Paper

Effects of pixel size on classification of microcalcifications on digitized mammograms
Author(s): Heang-Ping Chan; Berkman Sahiner; Nicholas Petrick; Kwok Leung Lam; Mark A. Helvie
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Paper Abstract

A database of 145 mammograms containing biopsy proven malignant or benign microcalcifications was digitized with a laser scanner at a pixel size of 35 micrometer by 35 micrometers. Digitization at larger pixel sizes was simulated by averaging adjacent pixels. The individual microcalcifications were segmented from the digital images with region growing and adaptive gray level thresholding techniques. The characteristics of the individual microcalcifications were analyzed with visibility descriptors and shape descriptors. The variations of visibility and shape of the microcalcifications in a cluster were evaluated by the standard deviation, the coefficient of variation, and the maximum of each of the descriptors. In addition, texture features were extracted from the spatial gray level dependence (SGLD) matrices in the region containing the cluster of microcalcifications. A genetic algorithm (GA) was used to select features from the multidimensional morphological and texture feature space. Linear discriminant analysis was employed to classify the microcalcifications as benign or malignant based on the selected features using a cross-validation training and test method. The classification accuracy was evaluated by the area, Az, under the receiver operating characteristic (ROC) curve. We varied the pixel size from 35 micrometers by 35 micrometers to 140 micrometers by 140 micrometers. The classification accuracy using morphological features decreased as the pixel size increased from 35 micrometers by 35 micrometers. In the texture feature space, the classification accuracy did not depend strongly on the pixel size. In the combined morphological and texture feature space, the classification accuracy also tended to decrease as the pixel size increased from 35 micrometer by 35 micrometer. The effect of spatial resolution on computerized classification depends on the features extracted for the classification task. We have not explored all possible features or feature parameters in this study and therefore the high spatial resolution information in the images may not have been fully exploited. Further studies to develop new features are being conducted and the impact of the spatial resolution needs to be further analyzed with a larger data set.

Paper Details

Date Published: 16 April 1996
PDF: 12 pages
Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); doi: 10.1117/12.237952
Show Author Affiliations
Heang-Ping Chan, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Nicholas Petrick, Univ. of Michigan (United States)
Kwok Leung Lam, Univ. of Michigan (United States)
Mark A. Helvie, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 2710:
Medical Imaging 1996: Image Processing
Murray H. Loew; Kenneth M. Hanson, Editor(s)

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