Haemorrhages detection using geometrical techniques

References

• Akram U, Shehzad K, Tariq A, Khan SA, Azam F. 2014. Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput Bio Med. 45:161–171.
   PubMed Web of Science ®Google Scholar
• Bharali P, Medhi JP, Nirmala SR. 2015. Detection of hemorrhages in diabetic retinopathy analysis using color fundus images. IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS); Kolkata, India. p. 237–242. doi:10.1109/ReTIS.2015.7232884.
   Google Scholar
• Dupas B, Walter T, Erginay A, Ordonez R, Deb-Joardar N, Gain P, Klein JC, Massin P. 2010. Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, hemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy. Diabetes Metab. 36:213–220.
   PubMed Web of Science ®Google Scholar
• Dutta MK, Parthasarathi M, Ganguly S, Srivastava K. 2015. An efficient image processing based technique for comprehensive detection and grading of non proliferative diabetic retinopathy from fundus images. Comput Methods Biomech Biomed Eng Imaging Vis. doi:10.1080/21681163.2015.1051187.
   Web of Science ®Google Scholar
• Faust O, Acharya R, Ng EYK, Kwan-Hoong N, Suri JS. 2012. Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review. J Med Syst. 36:145–157.
   PubMed Web of Science ®Google Scholar
• Fleming AD, Goatman KA, Philip S, Williams GJ, Prescott GJ, Scotland GS, McNamee P, Leese GP, Wykes WN, Sharp PF. 2010. The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy. Br J Ophthalmol. 94:706e711. 123–34. doi:10.1136/bjo.2008.149807.
   Web of Science ®Google Scholar
• Fleming AD, Philip S, Goatman KA, Olson JA, Sharp PF. 2006. Automated microaneurysms detection using local contrast normalization and local vessel detection. IEEE Trans Med Imaging. 25(9):167–184.
   Web of Science ®Google Scholar
• Garcia M, Sanchez CI, Lopez MI, Diez A, Hornero R. 2008. Automatic detection of red lesions in retinal images using a multilayer perceptron neural network. In Proceedings of 30th Annual International IEEE EMBS Conference Vancouver; British Columbia, Canada. p. 20–24.
   Google Scholar
• Grisan E, Ruggeri A. 2005. A hierarchical bayesian classification for non-vascular lesions detection in fundus images. In Proceedings of the 3rd European Medical and Biological Engineering Conference November, EMBEC’05; Prague; Czech Republic. p. 20–25.
   Google Scholar
• Grisan E, Ruggeri A. 2007. Segmentation of candidate dark lesions in fundus images based on local thresholding and pixel density. In Proceedings of the 29th Annual International Conference of the IEEE EMBS Cite International; August; Lyon, France. p. 23–26.
   Google Scholar
• Hatanaka Y, Nakagawa T, Hayashi Y, Hara T, Fujita H. 2008. Improvement of automated detection method of haemorrhages in fundus images. Proceedings of Annual International IEEE EMBS Conference Vancouver; British Columbia, Canada. p. 20–24.
   Google Scholar
• Kande GB, Savithri TS, Subbaiah PV. 2010 Aug. Automatic detection of microaneurysms and haemorrhages in digital fundus images. J Digit Imaging. 23(4):430–437. doi:10.1007/s10278-009-9246-0
   PubMed Web of Science ®Google Scholar
• Kauppi T, Kalesnykiene V, Kamarainen JK, Lensu L, Sorri I, Raninen A, Voutilainen R, Uusitalo H, Kalviainen H, Pietila J. 2007. DIARETDB1 diabetic retinopathy database and evaluation protocol. Proc Med Image Understanding Anal MIUA. 1:3–7.
   Google Scholar
• Likely new treatment target identified for diabetic retinopathy, Science news a service of the American association. 2017. Medical College Of Georgia At Augusta University, Nature Communications journal.
   Google Scholar
• Mane V, Jadhav DV. 2014. Progress towards automated early stage detection of diabetic retinopathy: image analysis systems and potential. J Med Biol Eng. 34(6):520–527.
   Web of Science ®Google Scholar
• Mane V, Jadhav DV. 2015. Detection of Red lesions in diabetic retinopathy affected fundus images. IEEE International Advance Computing Conference (IACC); June 12–13; Bangalore. p. 56–60, doi:10.1109/IADCC.2015.7154668.
   Google Scholar
• Mane V, Jadhav DV. 2017 May. Holoentropy enabled-decision tree for automatic classification of diabetic retinopathy using retinal fundus images. Biomed Eng/Biomedizinische Technik.62(3):321–332.
   PubMed Web of Science ®Google Scholar
• Mohammad Al-Jarrah A, Shatnawi H. 2017. Non-proliferative diabetic retinopathy symptoms detection and classification using neural network. J Med Eng Technol. doi:10.1080/03091902.2017.1358772.
   PubMedGoogle Scholar
• Mumtaz R, Hussain M, Sarwar S, Khan K, Mumtaz S, Mumtaz M. 2017. Automatic detection of retinal hemorrhages by exploiting image processing techniques for screening retinal diseases in diabetic patients. Int J Diabetes Dev Ctries. doi:10.1007/s13410-017-0561-6.
   Web of Science ®Google Scholar
• Roychowdhury S, Koozekanani DD, Parhi KK. 2014. DREAM: diabetic retinopathy analysis using machine learning. IEEE Journal of Biomedical and Health Informatics. 18(5):1717–1728. doi:10.1109/JBHI.2013.2294635.
   PubMed Web of Science ®Google Scholar
• Roychowdhury S, Koozekanani DD, Parhil KK. 2012. Screening fundus images for diabetic retinopathy. In Proceedings of Signals, Systems and Computers (ASILOMAR); Pacific Grove, CA, USA: IEEE Conference. p. 1614–1645. doi:10.1109/ACSSC.2012.6489310.
   Google Scholar
• Sengar N, Dutta M. 2017. Automated method for hierarchal detection and grading of diabetic retinopathy. Comput Methods Biomech Biomed Eng Imaging Vis. 1–11. doi:10.1080/21681163.2017.1335236.
   Web of Science ®Google Scholar
• Sharma A, Dutta MK, Singh A, Parthasarathi M. 2014. Dynamic thresholding technique for detection of hemorrhages in retinal images. Seventh International Conference on Contemporary Computing (IC3); Noida, India: IEEE. doi:10.1109/IC3.2014.6897158.
   Google Scholar
• Sinthanayothin C, Boyce JF, Williamson TH, Mensah HL,E, Lal S, Usher. D. 2002. Automated detection of diabetic retinopathy on digital fundus images. Diabetes UK. Diabetic Med. 19:105–112.
   PubMed Web of Science ®Google Scholar
• Srivastava R, Duana L, Wonga DWK, Liua J, Wong TY. 2016. Detecting retinal microaneurysms and hemorrhages with robustness to the presence of blood vessels. Comput Methods Programs Biomed. doi:10.1016/j.cmpb.2016.10.017.
   Web of Science ®Google Scholar
• Tang L, Niemeijer M, Reinhardt JM, Garvin MK, Abramoff MD. 2013. Splat feature classification with application to retinal hemorrhage detection in fundus images. IEEE Trans Med Imaging. 32(2):345–363.
   Web of Science ®Google Scholar
• Usher D, Dumskyj M, Himaga M, Williamson TH, Nussey S, Boyce J. 2004. Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabetes UK. Diabetic Med. 21:84–90.
   PubMed Web of Science ®Google Scholar
• Verma K, Prakash D, Ramakrishnan AG. 2011. Detection and classification of diabetic retinopathy using retinal images. In Proceedings of India Conference (INDICON); Annual IEEE, Hyd. p. 127–134.
   Google Scholar
• Xiao Z, Zhang X, Geng L, Zhang F, Wu J, Tong J, Ogunbona PO, Shan C. 2017. Automatic non-proliferative diabetic retinopathy screening system based on color fundus image. Bio Med Engg OnLine. 16:122. doi:10.1186/s12938-017-0414-z.
   PubMed Web of Science ®Google Scholar
• Zhou W, Wu C, Chen D, Wang Z, Yi Y, Du W. 2017. Automated detection of red lesions using super pixel multichannel multi feature. Comput Math Methods Med. 13:13. Article ID 9854825. doi:10.1155/2017/9854825.
   Google Scholar