Statistical-based linear vessel structure detection in medical images

Author

Adel, Mouloud ; Rasigni, Monique ; Gaidon, Thierry ; Fossati, Caroline ; Bourennane, Salah

Author_Institution

Inst. FRESNEL, Domaine Univ. de St.-Jerome, Marseille, France

fYear

2009

fDate

7-10 Nov. 2009

Firstpage

649

Lastpage

652

Abstract

Linear structures such as blood vessels in medical images are important features for computer-aided diagnosis and follow-up of many diseases. In this letter a new tracking-based segmentation method is proposed to detect blood vessels in retinal angiorams. Bayesian segmentation with the maximum a posteriori (MAP) probability criterion is used for that purpose. First promising results are presented and discussed.

Keywords

Bayes methods; image segmentation; maximum likelihood estimation; medical image processing; object detection; patient diagnosis; probability; statistical analysis; Bayesian segmentation; blood vessel detection; computer-aided diagnosis; maximum a posteriori probability criterion; medical images; retinal angiorams; statistical-based linear vessel structure detection; tracking-based segmentation method; Bayesian methods; Bifurcation; Biomedical imaging; Blood vessels; Image edge detection; Image segmentation; Iterative algorithms; Matched filters; Medical diagnostic imaging; Retina; Bayesian segmentation; retinal image;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing (ICIP), 2009 16th IEEE International Conference on

Conference_Location

Cairo

ISSN

1522-4880

Print_ISBN

978-1-4244-5653-6

Electronic_ISBN

1522-4880

Type

conf

DOI

10.1109/ICIP.2009.5413396

Filename

5413396