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Proceedings Paper

Computerized characterization of breast masses using three-dimensional ultrasound images
Author(s): Berkman Sahiner; Gerald L. LeCarpentier; Heang-Ping Chan; Marilyn A. Roubidoux; Nicholas Petrick; Mitchell M. Goodsitt; S. Sanjay-Gopal; Paul L. Carson
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Paper Abstract

Breast ultrasound can potentially increase the accuracy of computerized discrimination of malignant and benign masses. Newly developed 3D ultrasound techniques provide statistically richer information than conventional 2D ultrasound, and may therefore be better-suited for computerized statistical classification techniques. In this study, we investigated the feasibility of classifying solid breast masses using features extracted from 3D ultrasound images. Our data set consisted of seventeen biopsy-proven masses. Eight of the masses were malignant and nine were benign. The masses were identified by an experienced breast radiologist in the 3D volume, and a 3D ellipsoid containing the mass was defined. Spatial gray level dependence features were extracted from 2D slices in three regions, which were (1) the interior of the ellipse; (2) a disk-shaped region at the upper periphery of the ellipse; and (3) a disk-shaped region at the lower periphery of the ellipse. 2D analysis was performed by evaluating the classification accuracy of the features extracted from each slice. 3D analysis was performed by first averaging feature values from different slices into a single 3D feature, and then evaluating the classification accuracy. The best texture feature in this study achieved a classification accuracy of Az equals 0.97 for both 3D and 2D analysis. Our results indicate that the performance of 3D analysis is comparable to that of 2D analysis using the best available slice. Since the best 2D slice for texture analysis may not be known a-priori, this preliminary study suggests that 3D ultrasound may be beneficial for computerized breast mass characterization.

Paper Details

Date Published: 24 June 1998
PDF: 12 pages
Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); doi: 10.1117/12.310905
Show Author Affiliations
Berkman Sahiner, Univ. of Michigan Medical Ctr. (United States)
Gerald L. LeCarpentier, Univ. of Michigan Medical Ctr. (United States)
Heang-Ping Chan, Univ. of Michigan Medical Ctr. (United States)
Marilyn A. Roubidoux, Univ. of Michigan Medical Ctr. (United States)
Nicholas Petrick, Univ. of Michigan Medical Ctr. (United States)
Mitchell M. Goodsitt, Univ. of Michigan Medical Ctr. (United States)
S. Sanjay-Gopal, Univ. of Michigan Medical Ctr. (United States)
Paul L. Carson, Univ. of Michigan Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 3338:
Medical Imaging 1998: Image Processing
Kenneth M. Hanson, Editor(s)

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