Nonsupervised 3D registration of PET and MRI data using chamfer matching

Mangin, J. Francois; Frouin, Vincent; Bendriem, B.

doi:10.1109/nssmic.1992.301500

Cited by 3 publications

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Fast Nonsupervised 3D Registration of PET and MR Images of the Brain

Mangin

Frouin

Bloch

et al. 1994

J Cereb Blood Flow Metab

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Summary:We propose a fully nonsupervised methodol ogy dedicated to the fast registration of positron emission tomography (PET) and magnetic resonance images of the brain. First, discrete representations of the surfaces of interest (head or brain surface) are automatically ex tracted from both images. Then, a shape-independent sur face-matching algorithm gives a rigid body transforma tion, which allows the transfer of information between both modalities. A three-dimensional (3D) extension of the chamfer-matching principle makes up the core of this surface-matching algorithm. The optimal transformation is inferred from the minimization of a quadratic general ized distance between discrete surfaces, taking into ac count between-modality differences in the localization of A number of approaches to the analysis of phys iological data obtained from positron emission to mography (PET) require complementary anatomi cal information from another modality such as mag netic resonance imaging (MRI) (Mazziotta et aI., 1991). Since scans are not (and often cannot be) performed with perfectly reproducible patient posi tioning, increasing needs for accurate and reproduc ible three-dimensional (3D) registration methods have appeared. Most of the existing methods in volve user interaction. Procedural approaches, which rely on specific acquisition protocols [stereo taxic frames (Clarysse et aI., 1991), headholders (Bettinardi et aI., 1991), external markers (Koeppe et aI., 1991; Maguire et aI., 1991)], suffer from lack of versatility. Retrospective assisted approaches, 749 the segmented surfaces. The minimization process is ef ficiently performed via the precomputation of a 3D dis tance map. Validation studies using a dedicated brain shaped phantom have shown that the maximum registra tion error was of the order of the PET pixel size (2 mm) for the wide variety of tested configurations. The soft ware is routinely used today in a clinical context by the physiCians of the Service Hospitalier Frt!deric loliot (> 150 registrations performed). The entire registration process requires -5 min on a conventional workstation.

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Fast Nonsupervised 3D Registration of PET and MR Images of the Brain

Mangin

Frouin

Bloch

et al. 1994

J Cereb Blood Flow Metab

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Extremal points: definition and application to 3D image registration

Thirion

1994

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94

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This paper presents the eztmction of feature points,.geometrically invariant, which can be used as reliable landmarks for registmtion and recognition. Mainly we show that it is possible to eztmct automatically feature lines and points j h m 3D images, which are TObust and invariant with respect to rigid t m n s f o m , and enough precise to perfown the automatic registmtion. This eztends the possible applications of "the Marching Lines algorithm, previously described for eztremal lines eztraction. We introduce here a new kind of feature points : the Extremal Points, belonging to the eztremal lines, which allow us to find a point to point correspondances between SD images of the same subject. We present also ezperimental results of automatic registmtion with real data, which demonstrate the remarkable stability of those points.

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