Paper
11 May 1994 Three-dimensional deformable model for segmentation and tracking of anisotropic cine cardiac MR images
Alok Gupta, Tom O'Donnell, Ajit Singh
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Abstract
MR imaging is increasingly being used as a method for analyzing and diagnosing cardiac function. Segmentation of heart chambers facilitates volume computation, as well as ventricular motion analysis. Successful techniques have been developed for segmentation of individual 2D slices. However 2D models limit the description of a 3D phenomenon to two dimensions and use only 2D constraints. The resulting model lacks interslice coherency, making interslice interpolation necessary. In addition, the model is more susceptible to corruption due to noise local to one or more slices. We present work towards an approach to segmenting cine MR images using a 3D deformable model with rigid and nonrigid components. Past approaches have used models without rigid components or used isotropic CT data. Our model adaptively subdivides the mesh in response to the forces extracted from image data. Additionally, the local mesh of the model encodes surface orientation to align the model with the desired edge directions, a crucial constraint for distinguishing close anatomical structures. The modified subdivision algorithm preserves orientation of the elements by vertex ordering. We present results of segmenting two multi-slice cardiac MR image series with interslice resolutions of 8 and 4 mm/slice, and intraslice resolution of 1mm/pixel. We also include work in progress on tracking multislice, multiphase cine cardiac MR sequences with 4mm interslice, and 1mm intraslice resolution.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alok Gupta, Tom O'Donnell, and Ajit Singh "Three-dimensional deformable model for segmentation and tracking of anisotropic cine cardiac MR images", Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); https://doi.org/10.1117/12.175118
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Cited by 2 scholarly publications.
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KEYWORDS
3D modeling

Image segmentation

Chemical elements

Data modeling

Magnetic resonance imaging

3D image processing

Motion models

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