Role of GLCM Features in Identifying Abnormalities in the Retinal Images

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Shantala Giraddi 1,* Jagadeesh Pujari 2 Shivanand Seeri 1

1. BVB College Of Engg.and Technology Dept of CSE, Hubli, India

2. SDM College Of Engg.and Technology Dept of ISE, Dharwad, India

* Corresponding author.


Received: 15 Jan. 2015 / Revised: 20 Feb. 2015 / Accepted: 13 Mar. 2015 / Published: 8 May 2015

Index Terms

Texture features, Hard exudates, GLCM features, Back propagation neural network


Accurate detection of exudates in the diabetic retinal images is a challenging task. The images can have varying contrast and color characteristics. In this paper authors present the performance comparison of two feature extraction methods namely color intensity features and second order texture features based on GLCM. Authors have proposed and implemented new approach for GLCM feature calculation in which the input image is divided into number smaller blocks and GLCM features are computed on these blocks. The performance of each feature extraction method is evaluated using Back Propagation Neural Network (BPNN) classifier that is classifying the blocks as either abnormal block or normal block. With GLCM features, an accuracy of 76.6% was obtained and with color features an accuracy of 100% was obtained. It was found that color features are better in identifying true positives than GLCM based texture features. However use of GLCM features reduces the occurrence of false positives.

Cite This Paper

Shantala Giraddi, Jagadeesh Pujari, Shivanand Seeri,"Role of GLCM Features in Identifying Abnormalities in the Retinal Images", IJIGSP, vol.7, no.6, pp.45-51, 2015. DOI: 10.5815/ijigsp.2015.06.06


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