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Proceedings Paper

Generalized procrustean image deformation for subtraction of mammograms
Author(s): Walter F. Good; Bin Zheng; Yuan-Hsiang Chang; Xiao Hui Wang; Glenn S. Maitz
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Paper Abstract

This project is a preliminary evaluation of two simple fully automatic nonlinear transformations which can map any mammographic image onto a reference image while guaranteeing registration of specific features. The first method automatically identifies skin lines, after which each pixel is given coordinates in the range [0,1] X [0,1], where the actual value of a coordinate is the fractional distance of the pixel between tissue boundaries in either the horizontal or vertical direction. This insures that skin lines are put in registration. The second method, which is the method of primary interest, automatically detects pectoral muscles, skin lines and nipple locations. For each image, a polar coordinate system is established with its origin at the intersection of the nipple axes line (NAL) and a line indicating the pectoral muscle. Points within a mammogram are identified by the angle of their position vector, relative to the NAL, and by their fractional distance between the origin and the skin line. This deforms mammograms in such a way that their pectoral lines, NALs and skin lines are all in registration. After images are deformed, their grayscales are adjusted by applying linear regression to pixel value pairs for corresponding tissue pixels. In a comparison of these methods to a previously reported 'translation/rotation' technique, evaluation of difference images clearly indicates that the polar coordinates method results in the most accurate registration of the transformations considered.

Paper Details

Date Published: 21 May 1999
PDF: 12 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348559
Show Author Affiliations
Walter F. Good, Univ. of Pittsburgh (United States)
Bin Zheng, Univ. of Pittsburgh (United States)
Yuan-Hsiang Chang, Univ. of Pittsburgh (United States)
Xiao Hui Wang, Univ. of Pittsburgh (United States)
Glenn S. Maitz, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)

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