22 papers:
SEKE-2015-SaharAA #approach #case study #detection #image #modelling #prototype- A Case Study Approach: Iterative Prototyping Model Based Detection of Macular Edema in Retinal OCT Images (SS, SA, MUA), pp. 266–271.
SAC-2015-MottaMSMPC #3d #image #re-engineering- All-in-focus imaging technique used to improve 3D retinal fundus image reconstruction (DM, LdM, ACdS, RM, AP, LAVdC), pp. 26–31.
ICPR-2014-KarM #fuzzy #using- Extraction of Retinal Blood Vessel Using Curvelet Transform and Fuzzy C-Means (SSK, SPM), pp. 3392–3397.
- ICPR-2014-LermeRBPK #adaptation #image #segmentation
- Segmentation of Retinal Arteries in Adaptive Optics Images (NL, FR, IB, MP, EK), pp. 574–579.
- ICPR-2014-QureshiHA #probability #using
- A Probabilistic Model for the Optimal Configuration of Retinal Junctions Using Theoretically Proven Features (TAQ, AH, BAD), pp. 3304–3309.
- ICPR-2012-DaiBWT #segmentation
- Retinal vessel segmentation via Iterative Geodesic Time Transform (BD, WB, XW, YT), pp. 561–564.
- ICPR-2012-SanLH #detection
- Constrained-MSER detection of retinal pathology (GLYS, MLL, WH), pp. 2059–2062.
- ICPR-2012-WongLTYLTCW #automation #detection #image
- Detecting the optic cup excavation in retinal fundus images by automatic detection of vessel kinking (DWKW, JL, NMT, FY, BHL, YCT, CYlC, TYW), pp. 73–76.
- ICPR-2010-ConduracheMM #classification #segmentation
- An LDA-based Relative Hysteresis Classifier with Application to Segmentation of Retinal Vessels (AC, FM, AM), pp. 4202–4205.
- ICPR-2010-DuB #image #segmentation
- Retinal Image Segmentation Based on Mumford-Shah Model and Gabor Wavelet Filter (XD, TDB), pp. 3384–3387.
- ICPR-2010-JoshiSKRK #image #segmentation
- Vessel Bend-Based Cup Segmentation in Retinal Images (GDJ, JS, KK, PR, SRK), pp. 2536–2539.
- ICPR-2010-PengYZC #classification #segmentation #using
- Retinal Blood Vessels Segmentation Using the Radial Projection and Supervised Classification (QP, XY, LZ, YmC), pp. 1489–1492.
- ICPR-v3-2006-ChoeCLM #generative #image
- Optimal Global Mosaic Generation from Retinal Images (TEC, IC, MWL, GGM), pp. 681–684.
- ICPR-v4-2006-LiZZB #analysis #approach #automation #multi #segmentation #using
- A New Approach to Automated Retinal Vessel Segmentation Using Multiscale Analysis (QL, LZ, DZ, PB), pp. 77–80.
- ICPR-v1-2002-LaliberteGS #case study #comparative #image
- Registration and Fusion of Retinal Images: A Comparative Study (FL, LG, YS), pp. 715–718.
- ICPR-v1-2002-OsarehMTM #comparison #image #locality
- Comparison of Colour Spaces for Optic Disc Localisation in Retinal Images (AO, MM, BTT, RM), pp. 743–746.
SIGMOD-2000-HsuLG #image #information management #mining- Image Mining in IRIS: Integrated Retinal Information System (WH, MLL, KGG), p. 593.
- ICPR-v1-2000-BatistaPA
- Binocular Tracking and Accommodation Controlled by Retinal Motion Flow (JB, PP, HA), pp. 1171–1174.
- ICPR-v3-2000-LloretSLSV #image #using
- Retinal Image Registration Using Creases as Anatomical Landmarks (DL, JS, AML, AS, JJV), pp. 3207–3210.
- ICPR-v4-2000-ThaibaouiRB #approach #detection #fuzzy #image #logic
- A Fuzzy Logic Approach to Drusen Detection in Retinal Angiographic Images (AT, AR, PB), pp. 4748–4751.
- ICPR-1998-YogesanEB #analysis #image
- Texture analysis of retinal images to determine nerve fibre loss (KY, RHE, CJB), pp. 1665–1667.
- SAC-1995-ToliasTP #detection #fuzzy #image
- Detecting aneurysms in retinal images: fuzzy morphology vs. conventional methods (YAT, IBT, SMP), pp. 565–569.
