Euclidean distance mapping

Danielsson, Per-Erik

doi:10.1016/0146-664x(80)90054-4

Cited by 1,605 publications

(780 citation statements)

References 7 publications

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“…Many DTs have been described in the literature (city block, chessboard, and so on). In this article, we are in terested only in the DTs that accurately approximate the Euclidean distance (Danielsson, 1980;Borgefors, 1984).…”

Section: D Distance Mapmentioning

confidence: 99%

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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Section: D Distance Mapmentioning

confidence: 99%

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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“…As reported in [11], a distance map from the vessel tree data is computed by means of the Danielsson's distance map algorithm [10], implemented using the ITK scientific library.…”

Section: Automatic Trajectories Computationmentioning

confidence: 99%

Multi-trajectories automatic planner for StereoElectroEncephaloGraphy (SEEG)

et al. 2014

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Purpose StereoElectroEncephaloGraphy (SEEG) is done to identify the epileptogenic zone of the brain using several multi-lead electrodes whose positions in the brain are preoperatively defined. Intracranial hemorrhages due to disruption of blood vessels can cause major complications of this procedure (<1 %). In order to increase the intervention safety, we developed and tested planning tools to assist neurosurgeons in choosing the best trajectory configuration. Methods An automated planning method was developed that maximizes the distance of the electrode from the vessels and avoids the sulci as entry points. The angle of the guiding screws is optimized to reduce positioning error. The planner was quantitatively and qualitatively compared with manually computed trajectories on 26 electrodes planned for three patients undergoing SEEG by four neurosurgeons. Quantitative comparison was performed computing for each trajectory using (a) the Euclidean distance from the closest vessel and (b) the incidence angle. Results Quantitative evaluation shows that automatic planned trajectories are safer in terms of distance from the closest vessel with respect to manually planned trajectories. Qualitative evaluation performed by four neurosurgeons showed that the automatically computed trajectories would have been pre-E. De Momi (B) · C. Caborni · G. Ferrigno Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy e-mail: elena.demomi@polimi.it

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“…In the special case where S has a constant value for all pixels, algorithm 1 is identical to Danielsson's Euclidean DT [19] followed by a threshold. The rationale for Danielsson's algorithm is that while the definition (4) of the DT…”

Section: Analysis Of the Algorithmmentioning

confidence: 99%

“…Thus, a few pixels at the edge of X ⊕ S can be mistakenly considered as belonging to (X ⊕S) c . In most practical cases this is of no consequence, and it can be partially corrected by using a larger neighborhood in the raster scanning algorithm, as Danielsson [19] does with the 8SED algorithm.…”

Section: Analysis Of the Algorithmmentioning

confidence: 99%

Locally adaptable mathematical morphology using distance transformations

Cuisenaire

2006

Pattern Recognition

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We investigate how common binary mathematical morphology operators can be adapted so that the size of the structuring element can vary across the image pixels.We show that when the structuring elements are balls of a metric, locally adaptable erosion and dilation can be efficiently implemented as a variant of distance transformation algorithms. Opening and closing are obtained by a local threshold of a distance transformation, followed by the adaptable dilation.

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Euclidean distance mapping

Cited by 1,605 publications

References 7 publications

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

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

Multi-trajectories automatic planner for StereoElectroEncephaloGraphy (SEEG)

Locally adaptable mathematical morphology using distance transformations

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