Paper
21 May 1999 3D reconstruction of clustered microcalcifications from two mammograms: information preservation
Rainer Stotzka, Juergen Haase, Tim Oliver Mueller
Author Affiliations +
Abstract
This work describes the three-dimensional reconstruction of clustered microcalcifications based on only two digitized mammograms. First, the mammograms are examined separately to detect suspicious areas automatically. A further investigation separates microcalcifications from other structures. Based on an optimized region matching and on a specially adapted inverse discrete radon-transformation the corresponding volume is estimated from two projections and visualized by a continuously rotating object. But do two projections of a cluster carry enough information to reconstruct its three- dimensional arrangement sufficiently? We use Shannon's definition of information to estimate a lower bound of preserved information, described as the ratio of average information contained in the projections and average information contained in the volume, for simplified scenarios. Assuming two orthogonal projections of a cubic volume containing k binary representations of microcalcification positions the average information in the projections is determined by the combinatorial quantity of admissible arrangements and the size n3 of the volume. The combinatorial quantity of legal three-dimensional arrangements of microcalcification positions describes the average information carried by the volume. We showed that the amount of preserved information in the projections is more than 95% if k equals n/2 positions are found in both projections; it will exceed 98% if k equals n/4 positions are set.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rainer Stotzka, Juergen Haase, and Tim Oliver Mueller "3D reconstruction of clustered microcalcifications from two mammograms: information preservation", Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); https://doi.org/10.1117/12.348534
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Cited by 5 scholarly publications.
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KEYWORDS
Mammography

Visualization

Image segmentation

3D modeling

Ions

Breast

Binary data

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