Professor Franjo Pernuš
Dept. of Electrical Engineering
University of Ljubljana (UL)
Trzaska 25, 1000 Ljubljana
Slovenia
Tel: +386 1 4768 312
Fax: +386 1 4768 279
e-mail: franjo.pernus@fe.uni-lj.si
URL: http://lit.fe.uni-lj.si/
Short CV
Franjo Pernus received the Diploma, M.S., and Ph.D. degrees in Electrical
Engineering from the University of Ljubljana, Ljubljana, Slovenia, in 1976, 1979, and
1991, respectively. In 1981 he received the Government of Canada Award for Foreign
Nationals and spent a year at McGill University, Montreal, Canada. Since 1976 he is
with the Department of Electrical Engineering, University of Ljubljana, where he is
currently Professor and Head of the Imaging Technologies Lab. His research interests
are in biomedical image processing and analysis, computer vision, and the
applications of image processing and analysis techniques to various biomedical and
industrial problems. He is (co)author of over 150 refereed scientific articles on
biomedical image processing and computer vision and has supervised 6 PhD students.
Currently, Franjo Pernus is Associate Editor of the IEEE Transactions on Medical
Imaging (2002-) and Computer Aided Surgery (2008-). In the past he was Associate
Editor of Pattern Recognition Letters (2004-2006) and of Electrotechnical Review
(1996-2008). He co-organized the 1st Workshop on Biomedical Image Registration
(1999) and was Guest Co-Editor of the special issue on Biomedical Image
Registration of the Image Vision Computing Journal. From 2002-2006 he was the
Chair of Technical Committee 9 (Biomedical Image Analysis) of the International
Association for Pattern Recognition and is a member of the IEEE BISP Technical
Committee (SP Society) since 2005. From 1997 to 2001 he was the President of the
Slovenian Pattern Recognition Society. Prof. Pernus is co-founder of Sensum, a
company, which supplies machine vision solutions for the pharmaceutical industry.
Abstract
3D/2D Registration for Image-Guided Medical Interventions
Medical imaging is currently undergoing rapid development with a strong emphasis
being placed on the use of imaging technology to render interventions (surgery,
radiotherapy, radiosurgery, biopsy, etc.) less and less invasive and to improve the
accuracy with which a given intervention can be performed compared to conventional
methods. In image-guided interventions, pre-intervention medical data, usually 3D
computed tomography (CT) or magnetic resonance (MR) images are used to
diagnose, plan, simulate, guide or otherwise assist a surgeon, radiotherapist, or
possibly a robot, in performing an intervention. The plan is constructed in the
coordinate system relative to pre-intervention data, while the intervention is
performed in the coordinate system relative to the patient. The relationship or spatial
transformation between pre-intervention data and plan and physical space occupied by
the patient during intervention is established by registration. Registration, which is the
crucial part of image-guided interventions, allows any 3D point defined in the preintervention image to be precisely located on the actual patient and may thus provide
the radiotherapist valuable information about the position of the patient (tumor) or the
surgeon about position of his instruments relative to the planned trajectory, nearby
vulnerable structures, or the ultimate target. To be suitable for a clinical application, a
registration algorithm must satisfy several requirements which concern registration
accuracy, robustness, computation time, and complexity and invasiveness of intraintervention data acquisition.
A brief overview of existing methods, three novel methods for registering 3D CT or
MR images to 2D X-ray images, the problem of 3D/2D registration validation, and
some quantitative registration results will be presented. The novel registration
methods are based solely on the information present in 2D and 3D images. They don’t
require fiducial markers, intra-operative X-ray image segmentation, or construction of
digitally reconstructed radiographs. The originality of the first approach is in using
normals to bone surfaces, preoperatively defined in 3D MR or CT images, and
gradients of intra-operative X-ray images at locations defined by rays, emitted from
the X-ray source and passing through 3D surface points. The registration is concerned
with finding the rigid transformation of a CT or MR volume, which provides the best
match between surface normals and back projected gradients. The second method
requires that first a 3D image is reconstructed from a few 2D X-ray images after
which the preoperative 3D image is brought into the best possible spatial
correspondence with the reconstructed image by optimizing a similarity measure.
Because the quality of the reconstructed image is generally low, a novel similarity
measure, which is able to cope with low image quality as well as with different
imaging modalities, has been designed. The third method is an extension and
improvement of the first method. The methods have been tested on two publicly
available lumbar spine data with known “gold standard” registrations.