For our laboratory, it represents the second Ph.D. thesis in the field of medical image processing, after Jean-Philippe Thiran's pioneering work, published in July 1997. My first thanks go to Jean-Philippe for starting this activity in TELE and opening the track for me and others.
The present study of distance transformations started as a side activity to the main research track of our medical image analysis group: the study of non-rigid registration methods for matching brain scans with anatomical atlases. Over the years, this side activity has taken more and more of my time as I came to realize the richness of the problem. I am profoundly grateful to Benoî t Macq, my supervisor, for giving me the freedom to do so. Benoît has also been the one who convinced me to join the laboratory in 1995, and has been a constant support through the years.
In TELE, I have had the chance to collaborate very closely with Pierre Charbonnier, then with Matthieu Ferrant. Together we explored fascinating topics such as the template-based segmentation of brain structures using a brain atlas and active surfaces, or the volumetric finite element modeling of brain deformations. Working with Pierre and Matthieu has been both an inspiring and joyful experience. I must also thank all the other members of TELE for the great social and intellectual atmosphere that they contribute to create in our laboratory.
Beyond TELE, I have had the opportunity to interact with many other teams within the UCL, in particular with the Positron Tomography Laboratory (TOPO), the Neural Rehabilitation Engineering Laboratory (GREN), the Neuro-physiology Laboratory (NEFY) and the Radiology Unit at St-Luc Hospital (RDGN). I could not possibly list all the people in those teams, and therefore I will restrict myself to two, with all due excuses to the others.
Christian Michel (TOPO) has been a constant support ever since he co-supervised my master thesis in 1995. Although he is probably one of the hardest-working researchers at the UCL, he is always remarkably available for any scientific discussion. He has also provided me with many of the images I worked upon.
Eduardo Romero (GREN) has been the physician with whom I collaborated most closely. His enthusiasm to develop the automatic morphometry of nerve cross-sections has been the trigger that led to the variety of applications that are presented in this work. He has also provided me with all the data sets needed for chapter 4, and has made significant contributions to the validation part of that chapter.
Beyond the UCL, I wish to thank the people of the Surgical Planning Laboratory at the Brigham and Women's Hospital, Harvard Medical School, Boston, MA. They have welcomed me in their amazing research environment in March, July and August 1999. Ron Kikinis suggested that I apply my algorithms to virtual endoscopy. Simon Warfield supervised my work last summer and first suggested that I apply my knowledge of distance transformations to k-NN classification. My thanks go to them and many others at the SPL.
My thanks also go to Gunilla Borgefors, whose articles on chamfer distances are probably the most referred to in the field. Although we never met in person, she sent me an very nice encouraging email a few years ago, after reading one of my first papers. Similarly, Max Viergever was the chairman of the oral session where I presented my first paper at SPIE Medical Imaging 1996. In both cases, their generous encouragement was a great source of motivation for the apprentice researcher I was.
Of course, I must also thank the members of my jury for the in-depth reading of this text and the various corrections they suggested.
Finally, I cannot fail to thank my parents, my family and my friends for their support throughout these years. In particular, I wish to thank Zsuzsanna, Karam, Cristi, Ovidiu, Nikos, Michelangelo, Laurent, Giu-seppe, Paolo, Jonas, Jari, Guy, Yannis and Marit, friends from all over Europe that worked with me in the Summer Program and Educational Committees of the Board of European Students of Technology (BEST). I will always remember the time I spent with them as some of the best of my life.