A new definition for fuzzy attributed graph homomorphism with application to structural shape recognition in brain imaging

Author

Perchant, Aymeric ; Bloch, Isabelle

Author_Institution

Ecole Nat. Superieure des Telecommun., Paris, France

Volume

3

fYear

1999

fDate

1999

Firstpage

1801

Abstract

We propose in this paper an original definition for fuzzy attributed graph homomorphism that deals with both the structural aspect and the maximization of similarity between attributes (on nodes and on arcs). A graph fuzzy homomorphism theory is developed to gather different pieces of information extracted from images, and to take full advantage of the whole graph structure, with several attached attributes. The homomorphism properties allow relevant and accurate graph mapping by evaluating graph structure deformation with fuzzy sets. Anticipated application is atlas-based labeling of brain MRI using graph homomorphism for spatial and temporal analysis of brain tumors and surrounding structures. The definitions and properties proposed in this paper are a first step towards this goal

Keywords

biomedical MRI; brain; fuzzy set theory; graph theory; image classification; image matching; image segmentation; mathematical morphology; medical image processing; arcs; atlas-based labeling; brain MRI; brain imaging; brain tumors; fuzzy attributed graph homomorphism; graph mapping; image matching; image recognition; image segmentation; maximization; nodes; similarity between attributes; spatial analysis; structural aspect; structural shape recognition; temporal analysis; Brain; Character recognition; Fuzzy sets; Image recognition; Image segmentation; Image sequences; Labeling; Layout; Magnetic resonance imaging; Structural shapes;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference, 1999. IMTC/99. Proceedings of the 16th IEEE

Conference_Location

Venice

ISSN

1091-5281

Print_ISBN

0-7803-5276-9

Type

conf

DOI

10.1109/IMTC.1999.776131

Filename

776131