Title :
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
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;
Conference_Titel :
Image Processing (ICIP), 2009 16th IEEE International Conference on
Conference_Location :
Cairo
Print_ISBN :
978-1-4244-5653-6
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2009.5413396
