Abstract
Diabetic Retinopathy (DR) is one of the major causes of blindness in diabetic patients. Early detection is required to reduce the visual impairment causing damage to eye. Microaneurysms are the first clinical sign of diabetic retinopathy. Robust detection of microaneurysms in retinal fundus images is critical in developing automated system. In this paper we present a new technique for detection and localization of microaneurysms using Dual tree complex wavelet transform and log Gabor features. Retinal blood vessels are eliminated using minor and major axis properties and correlation is performed on images with the Gabor features to detect the microaneurysms. Feature vectors are extracted from candidate regions based on texture properties. Support vector machine classifier classifies the detected regions to determine the findings as microaneurysms or not. Accuracy of the algorithm is evaluated using the sensitivity and specificity parameters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Guidelines for the Comprehensive Management of Diabetic Retinopathy in India:A Vision 2020 The Right to Sight INDIA (2008)
Antal, B., Hajdu, A.: Improving microaneurysms detection in color fundus images by using an optimal combination of preprocessing methods and candidate extractors. In: 18th European Signal Processing Conference (EUSIPCO 2010), Aalborg, Denmark (2010)
Bhalerao, A., Patanaik, A., Anand, S., Saravanan, P.: Robust Detection of Mircoaneurysms for Sight Threatening Retinopathy Screening. In: Proceedings of Sixth Indian Conference of Computer Vision, Graphics & Image Processing, pp. 520–527 (2008)
Sopharak, A., Uyyanonvara, B., Barman, S., Williamson, T.: Automatic Microaneurysm Detection from Non-dilated Diabetic Retinopathy Retinal Images. In: Proceedings of the World Congress on Engineering, vol. II, pp. 1583–1586 (2011)
Mizutani, A., Muramatsu, C., Hatanaka, Y., Suemori, S., Hara, T., Fujita, H.: Automated Microaneurysm detection method based on double ring filter in retinal fundus images. In: Proceedings of SPIE. Medical Imaging, vol. 7260 (2009)
Lazar, I., Hajdu, A.: Microaneurysm Detection in Retinal Images using a Rotating Cross-Section based Model. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro (2011)
Joseph, C.R., Alexander, J.: Microaneurysm Detection and Diabetic Retinopathy Grading Using Candidate Extraction Algorithm. International Journal of Research in Engineering & Advanced Technology 1(1) (2013)
Selesnick, I.W., Baraniuk, R.G., Kingsbury, N.G.: The dual-tree complex wavelet transform. IEEE Signal Proc. Mag., 123–151 (2005)
Kovesi, P.: Image features from phase congruency. Videre Journal of Computer Vision Research, 1–27 (1999)
Kovesi, P.: Phase congruency detects corners and edges. In: DICTA, Sydney (2003)
Lee, J., Zee, B., Li, Q.: Segmentation and Texture analysis with Multi-model Inference for the Automatic Detection of Exudates in Early Diabetic Retinopathy. Journal of Biomedical Science and Engineering 6, 298–307 (2013)
Wisaeng, K., Hiransakolwong, N., Pothiruk, E.: Automatic Detection of Retinal Exudates using a Support Vector Machine. Applied Medical Informatics 32(1), 33–42 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Angadi, S., Ravishankar, M. (2015). Detection and Classification of Microaneurysms Using DTCWT and Log Gabor Features in Retinal Images. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 328. Springer, Cham. https://doi.org/10.1007/978-3-319-12012-6_65
Download citation
DOI: https://doi.org/10.1007/978-3-319-12012-6_65
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12011-9
Online ISBN: 978-3-319-12012-6
eBook Packages: EngineeringEngineering (R0)