Literature DB >> 10711993

Registration and difference analysis of corresponding mammogram images.

M Y Sallam1, K W Bowyer.   

Abstract

An automated technique is proposed for identifying differences between corresponding mammogram images. The technique recovers an approximate deformation between a pair of mammograms based on identifying corresponding features across the two images. The registration process is completed using an unwarping technique for transforming one image into the coordinate system of the other. A difference image is generated using intensity-weighted subtraction in order to identify regions of large difference. Evaluation of the technique is performed using 124 bilateral image pairs which contain a total of 77 abnormalities of different types. The purpose of this paper is to measure the extent to which the mammogram registration technique is able to provide useful information for identifying abnormalities in mammograms.

Mesh:

Year:  1999        PMID: 10711993     DOI: 10.1016/s1361-8415(99)80001-2

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  4 in total

1.  Matching breast masses depicted on different views a comparison of three methods.

Authors:  Bin Zheng; Jun Tan; Marie A Ganott; Denise M Chough; David Gur
Journal:  Acad Radiol       Date:  2009-07-25       Impact factor: 3.173

2.  Optical mammography: bilateral breast symmetry in hemoglobin saturation maps.

Authors:  Pamela G Anderson; Angelo Sassaroli; Jana M Kainerstorfer; Nishanth Krishnamurthy; Sirishma Kalli; Shital S Makim; Roger A Graham; Sergio Fantini
Journal:  J Biomed Opt       Date:  2016-10       Impact factor: 3.170

3.  Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms.

Authors:  Xuxin Chen; Ke Zhang; Neman Abdoli; Patrik W Gilley; Ximin Wang; Hong Liu; Bin Zheng; Yuchen Qiu
Journal:  Diagnostics (Basel)       Date:  2022-06-25

4.  Deep learning can be used to train naïve, nonprofessional observers to detect diagnostic visual patterns of certain cancers in mammograms: a proof-of-principle study.

Authors:  Jay Hegdé
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-04
  4 in total

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