Combining interpolation and 3D level set method (I+3DLSM) for medical image segmentation | Zendy

Doshi T. | Zendy; Di Caterina G. | Zendy; Soraghan J. | Zendy; Petropoulakis L. | Zendy; Grose D. | Zendy; MacKenzie K. | Zendy; Wilson C. | Zendy

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Combining interpolation and 3D level set method (I+3DLSM) for medical image segmentation

Author(s) -

Doshi T.,

Di Caterina G.,

Soraghan J.,

Petropoulakis L.,

Grose D.,

MacKenzie K.,

Wilson C.

Publication year - 2016

Publication title -

electronics letters

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.375

H-Index - 146

ISSN - 1350-911X

DOI - 10.1049/el.2015.3959

Subject(s) - interpolation (computer graphics) , artificial intelligence , image segmentation , image scaling , computer vision , set (abstract data type) , image (mathematics) , level set (data structures) , level set method , segmentation , computer science , scale space segmentation , pattern recognition (psychology) , mathematics , image processing , programming language

A combined interpolation – three‐dimensional level set method (3D LSM)‐based segmentation process is presented. The performance in terms of accuracy of the 3D LSM in the segmentation of throat regions from highly anisotropic magnetic resonance imaging (MRI) volumes, with and without an interpolation step is evaluated. Qualitative and quantitative results from real MRI data suggest that performing interpolation, to reconstruct isotropic MRI volumes, prior to 3D LSM improves the accuracy of the segmentation results, compared to interpolation post 3D LSM and no interpolation at all.

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