Literature DB >> 17300915

From MIP image to MRA segmentation using fuzzy set theory.

Maximilien Vermandel1, Nacim Betrouni, Christian Taschner, Christian Vasseur, Jean Rousseau.   

Abstract

The aim of this paper is to describe a semi-automatic method of segmentation in magnetic resonance angiography (MRA). This method, based on fuzzy set theory, uses the information (gray levels) contained in the maximum intensity projection (MIP) image to segment the 3D vascular structure from slices. Tests have been carried out on vascular phantom and on clinical MRA images. This 3D segmentation method has proved to be satisfactory for the detection of vascular structures even for very complex shapes. Finally, this MIP-based approach is semi-automatic and produces a robust segmentation thanks to the contrast-to-noise ratio and to the slice profile which are taken into account to determine the membership of a voxel to the vascular structure.

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Year:  2007        PMID: 17300915     DOI: 10.1016/j.compmedimag.2006.12.002

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  2 in total

1.  Three-dimensional skeletonization and symbolic description in vascular imaging: preliminary results.

Authors:  L Verscheure; L Peyrodie; A S Dewalle; N Reyns; N Betrouni; S Mordon; M Vermandel
Journal:  Int J Comput Assist Radiol Surg       Date:  2012-07-31       Impact factor: 2.924

2.  Algorithms for segmenting cerebral time-of-flight magnetic resonance angiograms from volunteers and anemic patients.

Authors:  Alexander Saunders; Kevin S King; Stefan Blüml; John C Wood; Matthew Borzage
Journal:  J Med Imaging (Bellingham)       Date:  2021-04-28
  2 in total

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