In 1991 we reviewed the user needs from the clinical disciplines of Neuroradiology, Neurosurgery, and Radiotherapy Planning for computer assistance, in particular for medical image analysis (prior to writing our proposal for the COVIRA project ). When, based on the perspective requirements, we derived specifications for algorithms which we would have to develop and related these to the state-of-the-art at that time, it became obvious that image segmentation and multimodality image registration would represent major bottlenecks. Meanwhile, around 400 manmonths of effort have been spent on this topic in COVIRA alone, and multiples of this may have been spent in Europe as a whole. In view of this, the medical imaging project line, the meeting of representatives of all imaging-related European projects receiving funds under the AIM programme of the EC, decided to conduct a workshop on these topics in April 1994, a time when most of the algorithms had reached reasonable maturity and were being integrated into pilot application systems to undergo clinical validation until the end of 1994.
The time of our initial analysis co-incided with the publication of a critical article about the lack of theoretic understanding and, consequently, of robustness of state-of-the-art image segmentation approaches. In their article on “Ignorance, Myopia and Naivete in Computer Vision Systems” (CVGIP Image Understanding, Vol. 53 (1), 112-117, 1991), R.C. Jain and T.O. Binford describe their view of the underlying reasons for the poor status of the field which was characterised by the fact that although a number of competing and promising approaches to image segmentation existed, a thorough comparative evaluation and critical assessment regarding issues such as generality, theoretical foundation, stability with respect to intrinsic parameter variations and input image quality had not been achieved on an international scale.
In view of this, a cautious approach was taken in the COVIRA project: while some groups investigated simple but fast, partly interactive segmentation methods, other groups tried to work on more automatic methods which were based on theoretical models and knowledge about the image generation physics. Since from earlier projects we had learned that the clinical user must always have the final say, tools for interactive editing of contours and surfaces were also pursued. All the results of these activities, together with those of other European workers in this field, were reported during the workshop and are layed down in this book.
In the field of multimodality image registration, a similar state-of-the-art analysis had been done, largely based on experience from the MMOMS project funded by the European Community during the AIM exploratory action programme, in which also those clinical applications had been defined for which the acquisition of multimodality images would be justified. This topic is currently gaining attention in view of the booming field of image guided therapy where frameless stereotaxy is the first application to be pursued on a commercial basis.
As long as we refer to the determination of the geometrical relationship between two independently acquired image data sets from different modalities, and assuming that the image acquisition processes have not introduced significant geometrical distortions, only rigid or affine transformations (scaling, translation, rotation, skewing) have to be considered. When registering patient images with those in an anatomy atlas, the use of so-called elastic registration (or matching) has to be discussed. In the COVIRA project, only rigid registration has been investigated, which is derived from common surfaces or points of reference (fiducial markers or anatomical landmarks) in the different data sets.
While surfaces and, more easily, fiducial markers are most efficiently obtained from suitable image segmentation operations on the involved data sets, anatomical landmarks still have to be interactively specified by the operator because they cannot be adequately modelled. The fact that in this way, multimodality image registration generally relies on the results of image segmentation processes applied to the input images, shows that for the overall success as far as clinical use is concerned, the two areas of research cannot be separated. They have been rightly combined in the workshop for which this book represents the proceedings. We hope that this book will serve to give an overview over recent advances in this field and stimulate further progress.