Level sets for retinal vasculature segmentation using seeds from ridges and edges from phase maps

IEEE International Workshop on Machine Learning for Signal Processing, MLSP

Volumn , Issue , 2012, Pages

  Dizdaro, Bekir ^a,b   Ataer Cansizoglu, Esra ^b   Kalpathy Cramer, Jayashree ^c   Keck, Katie ^d   Chiang, Michael F ^d,e   Erdogmus, Deniz ^b  

^a KARADENIZ TECHNICAL UNIVERSITY   (Turkey)

^b NORTHEASTERN UNIVERSITY   (United States)

^c MASSACHUSETTS GENERAL HOSPITAL   (United States)

^d OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

^e OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

Author keywords

Fundus image; level sets; phase map for edge detection; principal curves as ridges; retinal vasculature analysis; vessel segmentation

Indexed keywords

FUNDUS IMAGE; LEVEL SET; PHASE MAPS; PRINCIPAL CURVE; RETINAL VASCULATURE; VESSEL SEGMENTATION;

EDGE DETECTION; LEARNING SYSTEMS; OPHTHALMOLOGY; SIGNAL PROCESSING;

IMAGE SEGMENTATION;

EID: 84870723393     PISSN: 21610363     EISSN: 21610371     Source Type: Conference Proceeding    
DOI: 10.1109/MLSP.2012.6349730     Document Type: Conference Paper

References (15)

1. Caselles V, Kimmel R, Sapiro G: Geodesic active contours. Int. J. Comput. Vis. 22 (1) (1997) 61-79. (Pubitemid 127507390)
2. Vese L, Chan T: A multiphase level set framework for image segmentation using the Mumford and Shah model. Int. J. Comput. Vis. 50 (3) (2002) 271-293. (Pubitemid 35377416)
3. Li C, Xu C, Gui C, Fox MD: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19 (12) (2010) 3243-3254.
4. Li C, Kao C, Gore JC, Ding Z: Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17 (10) 2008 1940-1949.
5. Marin D, Aquino A, Arias GME, Bravo JM: A New supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Medical Imaging. 30 (1) (2011) 146-158.
6. Soares JVB, Leandro JJG, Cesar RM Jr., Jelinek HF, Cree MJ: Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans. Med. Imaging. 25 (9) (2006) 1214-1222. (Pubitemid 44304280)
7. Lathen G, Jonasson J, Borga M: Blood vessel segmentation using multi-scale quadrature filtering. Pattern Recognition Letters. 31 (2010) 762-767.
8. Kovesi P: Phase congruency: A low-level image invariant. Psychological Research. 64 (2) (2000) 136-148.
9. Otsu N: A threshold selection method from gray-level histogram. IEEE Trans. System Man Cybernetics. 9 (1) (1979) 62-66. (Pubitemid 9413341)
10. Erdogmus D, Ozertem U: Self-consistent locally defined principal surfaces. Proceedings of ICASSP 2007 II.549-II.552. (Pubitemid 47177767)
11. You S, Bas E, Erdogmus D, Kalpathy-Cramer J: Principal curve based retinal vessel segmentation towards diagnosis of retinal diseases. Proceedings of Healthcare Informatics, Imaging and Systems Biology (HISB) 2011 331-337.
12. Staal J, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Medical Imaging 23 (2004) 501-509.
13. Osareh A, Shadgar B: An automated tracking approach for extraction of retinal vasculature in fundus images. Journal of Opthalmic Vision Research 5 (2010) 20-26.
14. Chiang MF, Jiang L, Gelman R, Du YE, Flynn JT: Interexpert agreement of plus disease diagnosis in retinopathy of prematurity. Arch Ophthalmol 125 (2007) 875-880. (Pubitemid 47076422)
15. Gelman R, Jiang L, Du YE, Martinez-Perez ME, Flynn JT, Chiang MF: Plus disease in retinopathy of prematurity: pilot study of computer-based and expert diagnosis. JAAPOS (2007) 532-540. (Pubitemid 350251263)