• Gy. Kovacs, A. Hajdu: A Self-Calibrating Approach for the Segmentation of Retinal Vessels by Template Matching and Contour Reconstruction, Medical Image Analysis 29 (2016), 24-46, IF=3.654.
  • Karimi, A. Hajdu: HTSFinder: Powerful Pipeline of DNA Signature Discovery by Parallel and Distributed Computing, Evolutionary Bioinformatics 12 (2016), 73-85, IF=1.452.
  • I. Lazar, A. Hajdu: Segmentation of retinal vessels by means of directional response vector similarity and region growing, Computers in Biology and Medicine (Elsevier) (2015), 66 (November 2015), 209-221, IF=1.240.
  • B. Harangi, A. Hajdu: Detection of the optic disc in fundus images by combining probability models, Computers in Biology and Medicine (Elsevier) (2015), 65 (October 2015), 10-24, IF=1.240.
  • Zs. Torok, T. Peto, E. Csosz, E. Tukacs, A. Molnar, A. Berta, J. Tozser, A. Hajdu, V. Nagy, B. Domokos, A. Csutak: Combined methods for diabetic retinopathy screening, using retina photographs and tear fluid proteomics biomarkers, Journal of Diabetes Research (2014), Paper 623619, 8 pages, IF=3.536.
  • B. Harangi, A. Hajdu: Automatic exudate detection by fusing multiple active contours and regionwise classification, Computers in Biology and Medicine (Elsevier) 54(1) (November 2014), 156-171, IF=1.475.
  • B. Antal, A. Hajdu: An ensemble-based system for automatic screening of diabetic retinopathy, Knowledge-Based Systems (Elsevier) 60 (April 2014), 20-27, IF=4.104.
  • Zs. Torok, T. Peto, E. Csosz, E. Tukacs, A. Marta Molnar, Zs. Maros-Szabo, A. Berta, J. Tozser, A. Hajdu, V. Nagy, B .Domokos, A. Csutak: Tear fluid proteomics multimarkers for diabetic retinopathy screening, BMC Opthalmology 13(40) (August 2013), 8 pages, IF=1.44.
  • A. Hajdu, L. Hajdu, A. Jonas, L. Kovacs, H. Toman: Generalizing the majority voting scheme to spatially constrained voting, IEEE Trans. on Image Processing 22(11) (November 2013), 4182-4194, IF=3.042.
  • B. Antal, A. Hajdu: Improving microaneurysm detection in color fundus images by using context-aware approaches, Computerized Medical Imaging and Graphics (Elsevier) 37(5-6) (September 2013), 403-408, IF=1.467.
  • I. Lazar and A. Hajdu: Retinal microaneurysm detection through local rotating cross-section profile analysis, IEEE Trans. on Medical Imaging 32(2) (February 2013), 400-407, IF=3.643.
  • Gy. Kovacs and A. Hajdu: Translation Invariance in the Polynomial Kernel Space and Its Applications in kNN Classification, Neural Processing Letters (Springer) (September 2012), 1-27, IF=1.088.
  • B. Antal, A. Hajdu: An Ensemble-based System for Microaneurysm Detection and Diabetic Retinopathy Grading, IEEE Trans. on Biomedical Engineering 59(6) (2012), 1720-1726. IF=2.278.
  • B. Antal, A. Hajdu, Zs. Szabo-Maros, Zs. Torok, A. Csutak, T. Peto: A Two-phase Decision Support Framework for the Automatic Screening of Digital Fundus Images, Journal of Computational Science (Elsevier) 3(5) (2012), 262-268.
  • B. Antal, A. Hajdu: Improving microaneurysm detection using an optimally selected subset of candidate extractors and preprocessing methods, Pattern Recognition 45 (2012), 255-261, IF=2.607.
  • R.J. Qureshi, L. Kovacs, B. Harangi, B. Nagy, T. Peto, A. Hajdu: Combining algorithms for automatic detection of optic disc and macula in fundus images, Computer Vision and Image Understanding (CVIU) 116 (2012), 138–145, IF=2.404.
  • B. Antal, A. Hajdu: A stochastic approach to improve macula detection in retinal images, Acta Cybernetica 20 (2011), 5-15.
  • A. Hajdu and I. Pitas: Piecewise linear digital curve representation and compression using graph theory and a line segment alphabet, IEEE Trans. on Image Processing 17/2 (February 2008), 126-133.
  • A. Hajdu and I. Pitas: Optimal approach for fast object-template matching, IEEE Trans. on Image Processing 16/8 (August 2007), 2048-2057.
  • A. Hajdu, J. Kormos, Zs. Lencse, L. Tron and M. Emri: The “MEDIP-Platform Independent Software System for Medical Image Processing” project, Journal of Universal Computer Science 12/9 (2006), 1229-1239.