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Proceedings Paper

Automatic recognition of bone for x-ray bone densitometry
Author(s): Larry A. Shepp; Y. Vardi; J. Lazewatsky; James Libeau; Jay A. Stein
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Paper Abstract

We described a method for automatically identifying and separating pixels representing bone from those representing soft tissue in a dual- energy point-scanned projection radiograph of the abdomen. In order to achieve stable quantitative measurement of projected bone mineral density, a calibration using sample bone in regions containing only soft tissue must be performed. In addition, the projected area of bone must be measured. We show that, using an image with a realistically low noise, the histogram of pixel values exhibits a well-defined peak corresponding to the soft tissue region. A threshold at a fixed multiple of the calibration segment value readily separates bone from soft tissue in a wide variety of patient studies. Our technique, which is employed in the Hologic QDR-1000 Bone Densitometer, is rapid, robust, and significantly simpler than a conventional artificial intelligence approach using edge-detection to define objects and expert systems to recognize them.

Paper Details

Date Published: 1 June 1991
PDF: 9 pages
Proc. SPIE 1452, Image Processing Algorithms and Techniques II, (1 June 1991); doi: 10.1117/12.45385
Show Author Affiliations
Larry A. Shepp, AT&T Bell Labs. (United States)
Y. Vardi, AT&T Bell Labs. (United States)
J. Lazewatsky, Hologic, Inc. (United States)
James Libeau, Hologic, Inc. (United States)
Jay A. Stein, Hologic, Inc. (United States)

Published in SPIE Proceedings Vol. 1452:
Image Processing Algorithms and Techniques II
Mehmet Reha Civanlar; Sanjit K. Mitra; Robert J. Moorhead II, Editor(s)

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