Open Access Journal

ISSN : 2394 - 6849 (Online)

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Open Access Journal

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

ISSN : 2394-6849 (Online)

Detection of Diabetic Retinopathy by Texture Analysis of Fundus Images

Author : Tejaswini G M 1 Mamtha Mohan 2

Date of Publication :10th May 2017

Abstract: Medical analysis is one of the fastest growing fields where a lot of recordings of patients are analyzed. Many researchers finding new technologies to automate the way of detection of the disease. Diabetic Retinopathy is one of the complex diabetics which can cause vision loss. Diabetic Retinopathy is detected by the presence of microaneurysms and exudates in the color fundus image of the eye. This work examines the analyzing capabilities in the texture of color fundus images to differentiate between diseased and healthy images. Aiming this, the performance of Local Binary Patterns as a texture descriptor for retinal images has been explored. The performance of the proposed algorithm is analyzed using parameters like sensitivity, specificity, and accuracy.

Reference :

  1. [1] World Health Organization (WHO), “Universal eye health: a global action plan 2014-2019,” 2013.

    [2] T. Ojala, M. Pietikinen, and T. Menp, “A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification,” in Advances in Pattern Recognition, 2nd International Conference on, 2001, pp. 397–406.

    [3] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 24, no. 7, pp. 971–987, 2002.

    [4] T. Ahonen, A. Hadid, and M. Pietikainen, “Face description with local binary patterns: Application to face recognition,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 28, no. 12, pp. 2037– 2041, 2006.

    [5] M. Heikkil, M. Pietikinen, and C. Schmid, “Description of interest regions with local binary patterns,” Pattern Recognition, vol. 42, no. 3, pp. 425 – 436, 2009.

    [6] S. Zabihi, M. Delgir, and H.-R. Pourreza, “Retinal vessel segmentation using color image morphology and local binary patterns,”


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