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Motion and deformation estimation from medical imagery by modeling sub-structure interaction and constraints

Authors
Sundaramoorthi, G.Hong, B.-W.Yezzi, A.
Issue Date
Sep-2012
Citation
Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications III - Proceedings of the International Symposium, CompIMAGE 2012, pp 221 - 227
Pages
7
Journal Title
Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications III - Proceedings of the International Symposium, CompIMAGE 2012
Start Page
221
End Page
227
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/48168
ISSN
0000-0000
Abstract
This paper presents a novel medical image registration algorithm that explicitly models the physical constraints imposed by objects or sub-structures of objects that have differing material composition and border each other, which is the case in most medical registration applications.Typical medical image registration algorithms ignore these constraints and therefore are not physically viable, and to incorporate these constraints would require prior segmentation of the image into regions of differing material composition, which is a difficult problem in itself. We present a mathematical model and algorithm for incorporating these physical constraints into registration /motion and deformation estimation that does not require a segmentation of different material regions. Our algorithm is a joint estimation of different material regions and the motion/deformation within these regions. Therefore, the segmentation of different material regions is automatically provided in addition to the image registration satisfying the physical constraints. The algorithm identifies differing material regions (sub-structures or objects) as regions where the deformation has different characteristics. We demonstrate the effectiveness of our method on the analysis of cardiac MRI which includes the detection of the left ventricle boundary and its deformation. The experimental results indicate the potential of the algorithm as an assistant tool for the quantitative analysis of cardiac functions in the diagnosis of heart disease.
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