Literature DB >> 12465263

Automatic image matching for breast cancer diagnostics by a 3D deformation model of the mamma.

N V Ruiter1, T O Müller, R Stotzka, H Gemmeke, J R Reichenbach, W A Kaiser.   

Abstract

X-ray mammograms and MR volumes provide complementary information for early breast cancer diagnosis. The breast is deformed during mammography, therefore the images can not be compared directly. A registration algorithm is investigated to fuse the images automatically. A finite element simulation was applied to a MR image of an underformed breast and compared to a compressed breast using different tissue models and boundary conditions. Based on the results a set of patient data was registered. To archive the requested accuracy distinguishing between the different tissue types of the breast was not necessary. A linear elastic model was sufficient. It was possible to simulate the deformation with an average deviation of approximately of the size of a voxel in the MRI data and retrieve the position of a lesion with an error of 3.8 mm in the patient data.

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Year:  2002        PMID: 12465263     DOI: 10.1515/bmte.2002.47.s1b.644

Source DB:  PubMed          Journal:  Biomed Tech (Berl)        ISSN: 0013-5585            Impact factor:   1.411


  3 in total

Review 1.  Deformable medical image registration: a survey.

Authors:  Aristeidis Sotiras; Christos Davatzikos; Nikos Paragios
Journal:  IEEE Trans Med Imaging       Date:  2013-05-31       Impact factor: 10.048

2.  Finite element modelling and validation for breast cancer detection using digital image elasto-tomography.

Authors:  Hina M Ismail; Chris G Pretty; Matthew K Signal; Marcus Haggers; J Geoffrey Chase
Journal:  Med Biol Eng Comput       Date:  2018-03-10       Impact factor: 2.602

3.  Multiscale Mechano-Biological Finite Element Modelling of Oncoplastic Breast Surgery-Numerical Study towards Surgical Planning and Cosmetic Outcome Prediction.

Authors:  Vasileios Vavourakis; Bjoern Eiben; John H Hipwell; Norman R Williams; Mo Keshtgar; David J Hawkes
Journal:  PLoS One       Date:  2016-07-28       Impact factor: 3.240

  3 in total

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