Top

Journal of Imaging Informatics in Medicine

Published in:

01-08-2010

Automatic Detection of Microaneurysms and Hemorrhages in Digital Fundus Images

Authors: Giri Babu Kande, T. Satya Savithri, P. Venkata Subbaiah

Published in: Journal of Imaging Informatics in Medicine | Issue 4/2010

Abstract

An efficient approach for automatic detection of red lesions in ocular fundus images based on pixel classification and mathematical morphology is proposed. Experimental evaluation of the proposed approach demonstrates better performance over other red lesion detection algorithms, and when determining whether an image contains red lesions the proposed approach achieves a sensitivity of 100% and specificity of 91%.

Spencer T, Olson J, McHardy K, Sharp P, Forrester J: An image processing strategy for the segmentation and quantification in fluorescein angiograms of the ocular fundus. Comput Biomed Res 29:284–302, 1996CrossRefPubMed

Fleming AD, Philip S, Goatman KA, Oslon JA, Sharp PF: Automated microaneurysms detection using local contrast normalisation and local vessel detection. IEEE Trans Med Imag 25(9):1223–1232, 2006CrossRef

Walter T, Klein JC: Automatic detection of microaneurysms in color fundus images of human retina by means of the bounding box closing. In: Proc. of Medical Data Analysis, 2002, pp 210–220

Niemeijer M, van Ginneken B, Staal J, Suttorp-Schulten MSA, Abrmoff MD: Automatic detection of red lesions in digital color fundus photograph. IEEE Trans Med Imag 24(5):584592, 2005

Hatanaka Y, Nakagawa T, Hayashi Y, Hara T, Fujita H: Improvement of automated detection method of hemorrhages in fundus images. In: 30th Annual International IEEE EMBS Conference, 2008, pp 5429–5432

Marino C, Ares E, Penedo ME, Ortega M, Barreira N, Gomez-ulla F: Automated three stage red lesions detection in digital color fundus images. WSEAS Trans Comput 7:207–215, 2008

Balasubramanian S, Pradhan S, Chandrasekaran V: Red lesions detection in digital fundus images. In: 15th IEEE Int Conf Image Proc, 2008, pp 2932–2935

Bhalerao A, Patanaik A, Anand S, Saravanan P: Robust detection of microaneurysms for sight threatening retinopathy screening. In: 6th Indian Conference on Computer Vision, Graphics & Image Processing, 2008, pp 520–527

Prasad S, Jain A, Mittal A: Automated feature extraction for early detection of diabetic retinopathy in fundus images. In: IEEE Conference on Computer Vision and Pattern Recognition, June 2009

10.

Fleming AD, Philip S, Goatman KA, Olson JA, Sharp PF: Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Trans Med Imag 25:1223–1232, 2006CrossRef

11.

Fleming AD, Philip S, Goatman KA, Olson JA, Sharp PF: Automated detection of blot haemorrhages as a sign of referable diabetic retinopathy. In: Proc. Medical Image Understanding and Analysis, 2008

12.

Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M: Detection of blood vessels in retinal images using two dimensional matched filters. IEEE Trans Med Imag 8:263–269, 1989CrossRef

13.

Chang C-I, Du Y, Wang J, Guo S-M, Thouin PD: Survey and comparative analysis of entropy and relative entropy thresholding techniques. IEEE Proc Vis Image Signal Process 153:837–850, 2006CrossRef

14.

Burges CJC: A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2:211–167, 1998CrossRef

15.

Haralick RM: Statistical and structural approaches to texture. Proc IEEE 67(5):786–804, 1979CrossRef

16.

Hoover A, Goldbaum M: Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans Med Imag 22(8):951–958, 2003CrossRef

17.

Kauppi T, Kalesnykiene V, Kamarainen JK, Lensu L, Sorri I, Uusitalo H, Kälviäinen H, Pietilä J: DIARETDB0: Evaluation database and methodology for diabetic retinopathy algorithms. Technical report. Lappeenranta University of Technology, Lappeenranta, Finland, 2006

18.

Kauppi T, Kalesnykiene V, Kamarainen JK, Lensu L, Sorri I, Uusitalo H, Kälviäinen H, Pietilä J: The DIARETDB1 diabetic retinopathy database and evaluation protocol. In Proc. of British Machine Vision Conference, 2007, pp 252–261

Title: Automatic Detection of Microaneurysms and Hemorrhages in Digital Fundus Images
Authors: Giri Babu Kande
T. Satya Savithri
P. Venkata Subbaiah
Publication date: 01-08-2010
Publisher: Springer-Verlag
Published in: Journal of Imaging Informatics in Medicine / Issue 4/2010
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-009-9246-0

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Automatic Detection of Microaneurysms and Hemorrhages in Digital Fundus Images

Abstract

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 4/2010

Web-based Training Method for Interpretation of Dental Images

The Clinical Application of a PACS-Dependent 12-Lead ECG and Image Information System in E-Medicine and Telemedicine

Customization of Medical Report Data

K-Bayes Reconstruction for Perfusion MRI II: Modeling and Technical Development

Vascular Editor: From Angiographic Images to 3D Vascular Models

Insight in Human Skin Microcirculation Using In Vivo Reflectance-Mode Confocal Laser Scanning Microscopy