Designing Fourier Descriptor-Based Geometric Models for Object Interpretation in Medical Images Using Genetic Algorithms

K Delibasis, P.E Undrill, G.G Cameron

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

In previous work we have modeled simple 3D anatomical objects using deformed superquadrics and established their optimal position with the aid of genetic algorithms (GAs). Here we extend the complexity of the search object using 3D Fourier descriptor (FD) representations and allow GAs once again to optimize the object's shape and position. Using magnetic resonance image data, we perform an approximate segmentation on one lateral ventricle in the brain and use the FDs from this as seeding values for the GAs to search for the left and right lateral ventricles in seven 3D data sets. We show that the method is capable of coping with normal biological variation. Finally, we compare FD/GA-guided segmentation with a manually guided interactive region growing method and find an agreement of 78 ± 10% in voxel classification with a corresponding average edge placement error of 2.2 ± 0.4 mm.
Original languageEnglish
Pages (from-to)286-300
Number of pages15
JournalComputer Vision and Image Understanding
Volume66
Issue number3
DOIs
Publication statusPublished - 1997

Fingerprint Dive into the research topics of 'Designing Fourier Descriptor-Based Geometric Models for Object Interpretation in Medical Images Using Genetic Algorithms'. Together they form a unique fingerprint.

  • Cite this