Extraction and reconstruction of retinal vasculature

References

• National Malaysian Eye Survey. Screening for Diabetic Retinopathy. 1996, Ministry of Health, Malaysia
   Google Scholar
• Disease Center: Wrong Diagnosis, Statistics by Country for Type 2 Diabetes. Available online at: http://www.wrongdiagnosis.com/d/diab2/stats-country.htm (accessed November 2004)
   Google Scholar
• Fang B., Hsu W., Lee M. L. (2003) Reconstruction of vascular structures in retinal images. IEEE International Conference on Image Processing, Barcelona, September, 14–172003. II: 157–160
   Google Scholar
• Zana F., Klein J. C. Segmentation of vessel-like patterns using morphology and curvature evaluation. IEEE Transactions on Image Processing 2001; 10(7)1010–1019
   PubMed Web of Science ®Google Scholar
• Hoover A., Kouznetsova V., Goldbaum M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging 2000; 19(3)337–346
   Google Scholar
• Ballerini L. Detection and quantification of foveal avascular zone alterations in diabetic retinopathy. Internet World Congress for Biomedical Sciences – INABIS 1998, On-line Proceedings of the 5th Internet World Congress for Biomedical Sciences 1998 (INABIS) at McMaster University, Canada, 7th–16th December. (Available online at: http://www.mcmaster.ca/inabis98/ophthalmology/ballerini0155/two.html)
   Google Scholar
• Ibanez M. V., Simo A. Bayesian detection of the fovea in eye fundus angiographies. Pattern Recognition Letters 1999; 20: 229–240
   Web of Science ®Google Scholar
• Li H., Chutatape O. Fundus image features extraction. Proceedings of the 22nd Annual EMBS International Conference, Chicago, IL, July, 23 – 282000, 3071–3073
   Google Scholar
• Elena M., Perez M., Hughes A. D., Stanton A. V., Thom S. A., Bharath A. A., Parker K. H. Segmentation of retinal blood vessels based on the second directional derivative and region growing. IEEE International Conference on Image Processing 1999; 2: 173–176
   Google Scholar
• Zhou L., Rzeszotarski M. S., Singerman L. J., Chokreff J. M. The detection and quantification of retinopathy using digital angiogram. IEEE Transactions on Medical Imaging 1994; 13(4)619–626
   PubMed Web of Science ®Google Scholar
• Chaudhuri S., Shatterjee S., Katz N., Nelson M., Goldbaum M. Detection of blood vessels in retinal images using two- dimensional matched filters. IEEE Transactions on Medical Imaging 1989; 8(3)263–269
   PubMed Web of Science ®Google Scholar
• Tamura S., Okamoto Y., Yanashima K. Zero-crossing interval correction in tracing eye fundus blood vessels. Pattern Recognition 1998; 21: 227–233
   Google Scholar
• Gregson P. H., Shien Z., Scott R. C., Kozousek V. Automated grading of venous beading. Computer and Biomedical Research 1995; 28: 291–304
   PubMedGoogle Scholar
• Zana F., Klein J. C. Robust segmentation of vessels from retinal angiography. IEEE 13th International Conference on Digital Signal Processing, Santorini, HellasGreece, July, 2 – 41997, 1087–1091
   Google Scholar
• Conforth D. J., Jelinek H. J., Leandro J. J.G., Soares J. V.B., Cesar R. M., Jr, Cree M. J., Mitchell P., Bossomaier T. (2004) Development of retinal blood vessel segmentation methodology using wavelet transforms for assessment of diabetic retinopathy. Eighth Asia Pacific Symposium on Intelligent and Evolutionary Systems, CairnsAustralia, December, 6 – 72004, 50–60
   Google Scholar
• Jin Y., Fayad L., Laine A. Contrast enhancement by multi-scale adaptive histogram equalization. Proceedings of SPIE 2001; 4478: 206–213
   Google Scholar
• Ngah U. K., Hai O. T., Khalid N. E.A. (2000) Mammographic calcification clusters using region growing technique. Proceedings of the New Millenium International Conference on Pattern Recognition & Robot Vision [TATI], TerengganuMalaysia, May, 14 – 152000
   Google Scholar
• Venkatachalam P. A., Fadzil M. H., Devan K. S. Computer-aided detection and diagnosis of microcalcifications in digital mammography. 2005, An IRPA Grant Project of University Technology PETRONAS
   Google Scholar
• Lee Y. B., Song S. M., Lee J. S., Kim M. H. (2005) Tumor segmentation from small animal PET using region growing based on gradient magnitude. IEEE International Conference on Communication, SeoulKorea, May, 16 – 202005
   Google Scholar
• Sato M., Lakare S., Wan M., Kaufman A., Nakajima M. A gradient magnitude based region growing algorithm for accurate segmentation. IEEE International Conference on Image Processing. 2000; 448–451
   Google Scholar
• Niemeijer M., Staal J., Ginnekan B. V., Loog M., Abramoff M. D. Comparative study of retinal vessel segmentation methods on a new publicly available database. SPIE Medical Imaging, J. M. Fitzpatrick, M. Sonka, 2004; 5370: 648–656, Available online at: http://www.isi.uu.nl/Research/Databases (accessed January 2006)
   Google Scholar
• Staal J. J., Abramoff M. D., Neimeijer M., Viergeer M. A., Ginneken B. V. Ridge based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 2004; 23: 501–509
   PubMed Web of Science ®Google Scholar
• Fritzsche K. H. Computer vision algorithms for retinal vessel width change detection and quantification. A proposal to conduct doctoral study. 2002, Available online at: http://www.cs.rpi.edu/∼fritzk2/ken_candidacy.pdf (accessed February 2006)
   Google Scholar
• Walter T., Klein J. C., Massin P., Erginay A. A contribution of image processing to the diagnosis of diabetic retinopathy—detection of exudates in color fundus images of the human retina. IEEE Transactions on Medical Imaging 2002; 21: 1236–1244
   PubMed Web of Science ®Google Scholar
• Jiang X., Mojon D. Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003; 25: 131–137
   Web of Science ®Google Scholar
• Perez M. M., Hughes A. D., Stanton A. V., Thom S. A., Bharath A. A., Parker K. H. Retinal Blood Vessel Segmentation by Means of Scale-Space Analysis and Region Growing. Lecture Notes in Computer Sciences (MICCAI 99), Cambridge, England, 19–22 September 1999; 1679: 90–97
   Google Scholar