This thesis presents research that was conducted to develop anatomically realistic finite element models of breast deformation under a variety of gravity loading conditions to assist clinicians in tracking suspicious tissues across multiple imaging modalities. Firstly, the accuracy of the modelling framework in predicting deformations of a homogeneous body was measured using custom designed silicon gel phantoms. The model predicted surface deformations with an average RMS error of 1.5 mm +/- 0.2 mm and tracked internal marker locations with an average RMS error of 1.4 mm +/- 0.7 mm. A novel method was then developed to determine the reference configuration of a body, when given its mechanical properties, boundary conditions and a deformed configuration. The theoretical validity of the technique was confirmed with an analytic solution. The accuracy of the method was also measured using silicon gel experiments, predicting the reference configuration surface with an average RMS error of 1.3 mm +/- 0.1 mm, and tracking internal marker locations with an average error of 1.5 mm +/- 0.8 mm. Silicon gel composites were then created to measure the accuracy of standard techniques to model heterogeneity. The models did not match the experimentally recorded deformations. This highlighted the need for further validation exercises on modelling heterogeneity before modelling them in the breast. A semi-automated algorithm was developed to fit finite element models to the skin and muscle surfaces of each individual, which were segmented from breast MR images. The code represented the skin with an average RMS error of 1.46 mm +/- 0.32 mm and the muscle with an average RMS error of 1.52 mm +/- 0.3 mm. The framework was then tested using images of the breast obtained under different gravity loading conditions and neutral buoyancy. A homogeneous model was first developed using the neutral buoyancy images as a representation of the reference configuration. The model did not accurately capture the regional deformations of the breast under gravity loading. However, the gross shape of the breast was reproduced, indicating that a biomechanical model of the breast could be useful to reliably track tissues across multiple images for cancer diagnosis. / This research was sponsored by the Top Achiever Doctoral Scholarship and the University of Auckland Doctoral Scholarship. Extra funding for travel was provided by the Graduate Research Fund and the John Logan Campbell Trust Fund.

This thesis presents research that was conducted to develop anatomically realistic finite element models of breast deformation under a variety of gravity loading conditions to assist clinicians in tracking suspicious tissues across multiple imaging modalities. Firstly, the accuracy of the modelling framework in predicting deformations of a homogeneous body was measured using custom designed silicon gel phantoms. The model predicted surface deformations with an average RMS error of 1.5 mm +/- 0.2 mm and tracked internal marker locations with an average RMS error of 1.4 mm +/- 0.7 mm. A novel method was then developed to determine the reference configuration of a body, when given its mechanical properties, boundary conditions and a deformed configuration. The theoretical validity of the technique was confirmed with an analytic solution. The accuracy of the method was also measured using silicon gel experiments, predicting the reference configuration surface with an average RMS error of 1.3 mm +/- 0.1 mm, and tracking internal marker locations with an average error of 1.5 mm +/- 0.8 mm. Silicon gel composites were then created to measure the accuracy of standard techniques to model heterogeneity. The models did not match the experimentally recorded deformations. This highlighted the need for further validation exercises on modelling heterogeneity before modelling them in the breast. A semi-automated algorithm was developed to fit finite element models to the skin and muscle surfaces of each individual, which were segmented from breast MR images. The code represented the skin with an average RMS error of 1.46 mm +/- 0.32 mm and the muscle with an average RMS error of 1.52 mm +/- 0.3 mm. The framework was then tested using images of the breast obtained under different gravity loading conditions and neutral buoyancy. A homogeneous model was first developed using the neutral buoyancy images as a representation of the reference configuration. The model did not accurately capture the regional deformations of the breast under gravity loading. However, the gross shape of the breast was reproduced, indicating that a biomechanical model of the breast could be useful to reliably track tissues across multiple images for cancer diagnosis. / This research was sponsored by the Top Achiever Doctoral Scholarship and the University of Auckland Doctoral Scholarship. Extra funding for travel was provided by the Graduate Research Fund and the John Logan Campbell Trust Fund.

Univ., Diss.--Mainz, 2005. / Literaturverz. S. 15 - 24.

Thesis (Ph.D.). / Written for the Dept. of Electrical & Computer Engineering. Title from title page of PDF (viewed 2008/01/12). Includes bibliographical references.

Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Oskar krinjar; Committee Member: Allen Tannenbaum; Committee Member: Anthony Yezzi; Committee Member: John Oshinski; Committee Member: Patricio Vela. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph. D.)--Carleton University, 1999. / Also available in electronic format on the Internet.

Thesis (Ph.D.)--University of Alberta, 2010. / A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Medical Physics, Department of Physics. Title from pdf file main screen (viewed on June 26, 2010). Includes bibliographical references.

Thesis (M.S.)--Mississippi State University. Department of Electrical and Computer Engineering. / Title from title screen. Includes bibliographical references.

This thesis belongs to the research line of Biomedical Imaging Tecnologies and proposes as main objective to develop and research spatio-temporal non-rigid registration methods to estimate and compesate motion in cardiac magnetic resonance sequences and to validate and verify the suitability of those techniques in the clinical environment. / -

Η ψηφιακή απεικόνιση είναι ένας ραγδαία αναπτυσσόμενος κλάδος στην εξέλιξη της τεχνολογίας των υπολογιστών και έχει γίνει ένα συμβατικό εργαλείο στην χαρτογράφηση της τηλεπισκόπησης. Η ανάγκη για καλύτερη ποιότητα απεικόνισης ενισχύει τον ψηφιακό τομέα να παράγει μεθόδους για την αποκατάσταση της γεωμετρικής παραμόρφωσης. Παρά το προχωρημένο επίπεδο της σημερινής τεχνολογίας, είναι γνωστό ότι οι συσκευές εισόδου και εξόδου, σαρωτές οι οποίοι είναι περιφερειακές συσκευές που αποτυπώνουν την εικόνα μιας περιοχής και χρησιμοποιούνται ως επί το πλείστον ως συσκευές εισόδου, προκαλούν παραμορφώσεις στη εικόνα. Στην παρούσα εργασία, γίνεται μια προσπάθεια να αντιμετωπισθούν λάθη στη γεωμετρία με χρήση του προγράμματος Matlab και της μεθόδου της αντιστοίχησης εικόνας. Η αντιστοίχηση εικόνας αποσκοπεί στην εύρεση αντίστοιχων σημείων σε δύο ή περισσότερες εικόνες, τα οποία αποτελούν προβολές του ίδιου σημείου της σκηνής. Η διαδικασία δειγματοληψίας των ψηφιακών εικόνων, το μοντέλο προβολής της σκηνής μέσω αισθητήρα όρασης στο επίπεδο των εικόνων και η κίνηση του αισθητήρα ή και της σκηνής, αποτελούν τους κύριους παράγοντες που καθιστούν το πρόβλημα της αντιστοίχησης αρκετά δύσκολο. Την πλειοψηφία των αλγορίθμων αντιστοίχησης εικόνας συνθέτουν οι παραμετρικές τεχνικές, σύμφωνα με τις οποίες υιοθετείται ένα παραμετρικό μοντέλο, το οποίο εφαρμοζόμενο στη μία εικόνα δύναται να παρέχει μια προσέγγιση της άλλης. Η προσέγγιση αυτή αξιολογείται μέσω ενός δείκτη συνολικού σφάλματος, ενώ η βέλτιστη δυνατή προσέγγιση επιτυγχάνεται με την εκτίμηση των τιμών των παραμέτρων του μοντέλου που βελτιστοποιούν τον δείκτη αυτό. Το βασικό σημείο του προτεινόμενου μοντέλου είναι η εφαρμογή πολυωνυμικών σχέσεων και η σωστή επιλογή του πολυωνυμικού γεωμετρικού μετασχηματισμού, λαμβάνοντας υπόψη τα χαρακτηριστικά της εκάστοτε παραμόρφωσης, για την αντιμετώπιση του προβλήματος. Λειτουργίες μετασχηματισμών χρησιμοποιούνται για να περιγράψουν τις γεωμετρικές διαφορές μεταξύ δύο εικόνων που έχουν το ίδιο περιεχόμενο. Λαμβάνοντας υπόψη τις συντεταγμένες ενός σημείου σε μια εικόνα ένας μετασχηματισμός θα καθορίσει τις συντεταγμένες του ίδιου σημείου στην άλλη εικόνα. Θα καλούμε μία από τις εικόνες δευτερεύουσα και την άλλη κύρια. Η κύρια εικόνα παραμένει αμετάβλητη ενώ η δευτερεύουσα παραμορφώνεται ώστε να έχει την γεωμετρία της κύριας εικόνας. Οι λειτουργίες των μετασχηματισμών για την αντιστοίχηση εικόνας καθορίζεται βάσει ενός αριθμού αντίστοιχων συντεταγμένων στις δύο εικόνες και επιλέγεται χειροκίνητα. Στην αντιστοίχηση εικόνας δίνονται και η κύρια και η δευτερεύουσα εικόνα με την δευτερεύουσα να παραμορφώνεται ώστε να αντιστοιχηθεί με την κύρια. / Digital imaging is a rapidly growing sector in the development of computer technology and has become a conventional tool in remote sensing mapping. The need for better image quality enhances the digital sector to produce methods for restoring the geometric distortion. Despite the advanced level of today’s technology, it is known that input and output devices, scanners that are peripheral devices that capture the image of a region, and are used mostly as an input device, cause distortions in the image. In this paper, by using the Matlab program and the method of image registration we try to deal with errors in geometry. Image registration aims to find corresponding points in two or more images which are projections of the same point of a scene. The resampling process of digital images, the projection model of the scene through vision sensor at the level of images and the motion of the sensor or scene’s , are the main factors of image mapping. The majority of algorithms of image registration compose the parametric techniques that adopt a parametric model which is applied to an image and can provide an approximation of another image. This approach is evaluated through a total error rate, while the optimal approximation is achieved by the evaluation of the values of the model parameters that optimize this indicator. The key point of the proposed model is the application of polynomial equations and the proper selection of polynomial geometric transformation. Transformation functions are used to describe geometric differences between two images that have the same or overlapping contents. Given the coordinates of a point in one image, a transformation function will determine the coordinates of the same point in the other image. We will call one of the images the slave and the other image the master. Master image is kept unchanged. Slave image needs to be deformed to have the geometry of the master image. The transformation functions for image registration are determined using the coordinates of a number of corresponding points in the images, selected manually. In image registration, both master and slave images are given, and the slave image is deformed to overlay the master image.