Mammogram CAD, hybrid registration and iconic analysis

A. Boucher; F. Cloppet; N. Vincent

doi:10.1117/12.2008512

6 March 2013 Mammogram CAD, hybrid registration and iconic analysis

A. Boucher, F. Cloppet, N. Vincent

Proceedings Volume 8661, Image Processing: Machine Vision Applications VI; 86610J (2013) https://doi.org/10.1117/12.2008512
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

This paper aims to develop a computer aided diagnosis (CAD) based on a two-step methodology to register and analyze pairs of temporal mammograms. The concept of "medical file", including all the previous medical information on a patient, enables joint analysis of different acquisitions taken at different times, and the detection of significant modifications. The developed registration method aims to superimpose at best the different anatomical structures of the breast. The registration is designed in order to get rid of deformation undergone by the acquisition process while preserving those due to breast changes indicative of malignancy. In order to reach this goal, a referent image is computed from control points based on anatomical features that are extracted automatically. Then the second image of the couple is realigned on the referent image, using a coarse-to-fine approach according to expert knowledge that allows both rigid and non-rigid transforms. The joint analysis detects the evolution between two images representing the same scene. In order to achieve this, it is important to know the registration error limits in order to adapt the observation scale. The approach used in this paper is based on an image sparse representation. Decomposed in regular patterns, the images are analyzed under a new angle. The evolution detection problem has many practical applications, especially in medical images. The CAD is evaluated using recall and precision of differences in mammograms.

Citation Download Citation

A. Boucher, F. Cloppet, and N. Vincent "Mammogram CAD, hybrid registration and iconic analysis", Proc. SPIE 8661, Image Processing: Machine Vision Applications VI, 86610J (6 March 2013); https://doi.org/10.1117/12.2008512

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