Section: Research Program

Introduction

Tremendous progress has been made in the automated analysis of biomedical images during the past two decades [90] . Readers who are neophytes to the field of medical imaging will find an interesting presentation of acquisition techniques of the main medical imaging modalities in [81] , [79] . Regarding target applications, a good review of the state of the art can be found in the book Computer Integrated Surgery [77] , in N. Ayache's article [85] and in recent review articles [86] , [90] . The scientific journals Medical Image Analysis [72] , Transactions on Medical Imaging [78] , and Computer Assisted Surgery [80] are also good reference material. One can have a good vision of the state of the art from the proceedings of the MICCAI'2010 (Medical Image Computing and Computer Assisted Intervention [75] , [76] ) and ISBI'2010 (Int. Symp. on Biomedical Imaging [74] ) conferences.

For instance, for rigid parts of the body like the head, it is now possible to fuse in a completely automated manner images of the same patient taken from different imaging modalities (e.g. anatomical and functional), or to track the evolution of a pathology through the automated registration and comparison of a series of images taken at distant time instants [91] , [105] . It is also possible to obtain from a Magnetic Resonance Image (MRI) of the head a reasonable segmentation of skull tissues, white matter, grey matter, and cerebro-spinal fluid [108] , or to measure some functional properties of the heart from dynamic sequences of Magnetic Resonance [84] , Ultrasound or Nuclear Medicine images [92] .

Despite these advances and successes, statistical models of anatomy are still very crude, resulting in poor registration results in deformable regions of the body, or between different subjects. If some algorithms exploit the physical modeling of the image acquisition process, only a few actually model the physical or even the physiological properties of the human body itself. Coupling biomedical image analysis with anatomical and physiological models of the human body could not only provide a better understanding of observed images and signals, but also more efficient tools for detecting anomalies, predicting evolutions, simulating and assessing therapies.