2005
DOI: 10.1007/11494621_4
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SPASM: Segmentation of Sparse and Arbitrarily Oriented Cardiac MRI Data Using a 3D-ASM

Abstract: Abstract. In this paper, a new technique (SPASM) based on a 3D-ASM is presented for automatic segmentation of cardiac MRI image data sets consisting of multiple planes with different orientations, and with large undersampled regions. SPASM was applied to sparsely sampled and radially oriented cardiac LV image data.Performance of SPASM has been compared to results from other methods reported in literature. The accuracy of SPASM is comparable to these other methods, but SPASM uses considerably less image data.

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Cited by 7 publications
(8 citation statements)
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“…The 3D extension of the algorithm (3D-ASM) was put forward by van Assen et al [3], using an optimized fuzzy inference strategy to provide the appearance (grey-level) model necessary to deform the shape model, and conduct the segmentation. This methodology allows for using sparse and evenly sampled image slices in any 3D position and orientation [4].…”
Section: LV Segmentationmentioning
confidence: 99%
“…The 3D extension of the algorithm (3D-ASM) was put forward by van Assen et al [3], using an optimized fuzzy inference strategy to provide the appearance (grey-level) model necessary to deform the shape model, and conduct the segmentation. This methodology allows for using sparse and evenly sampled image slices in any 3D position and orientation [4].…”
Section: LV Segmentationmentioning
confidence: 99%
“…The PDM is trained from a population of typical examples of the target shape, and models shape variability as a linear combination of a mean shape, and a number of eigenshapes. Three dimensional ASMs for cardiac segmentation were described by Van Assen et al [2] and Kaus et al [4].…”
Section: Methodsmentioning
confidence: 99%
“…The second part of the SPASM, the matching algorithm, is based on a Takagi-Sugeno Fuzzy Inference System (FIS) using Fuzzy C-means (FCM) [2] …”
Section: Spasm Matching: Feature Point Detectionmentioning
confidence: 99%
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