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An automated exudate detection scheme supporting diabetic retinopathy screening using spatial-spectral-statistical feature maps

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Abstract

An automated Diabetic Retinopathy (DR) introspection scheme for early detection of retinopathy signs is realized in this paper. Presence of exudates in retina is considered as an early sign of DR. Therefore, the proposed methodology aims at detection of exudates by transforming the acquired fundus image into a high-dimensional feature map labelled as Spatial-Spectral-Statistical (SSS) feature map that uniquely represents the individual image pixels using a novel characterization scheme. At the onset, a slightly novel pre-processing scheme is fused into the mechanism to address the non-uniform illumination issues present in images. Later, separate feature characterizers and descriptors pertaining to the diverse domains are engaged for extraction of the different features from the input fundus image. These distinct features are then blended to yield the feature map representing the given image. Then a supervised classifier categorizes these features and finally aids in deciding the presence or absence of exudates for the given input. Extensive investigation and relative comparisons performed on publicly available dataset namely DIARETDB0, DIARETDB1 and MESSIDOR demonstrate a consistent average classification accuracy of 97.99%, an attribute owed to the unique feature aggregation scheme that also, makes the methodology robust under different imaging problems.

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Availability of data and material

Three publicly available datasets namely DIARETDB0, DIARETDB1 and MESSIDOR are utilized for performance analysis of the presented methodology

Code availability

The code developed towards the realization of the framework discussed in the paper forms a part of the author’s research work and hence unavailable.

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This manuscript is a part of the research work executed by the author towards the Doctoral degree. Hence, no grants / funding were received for this work

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Authors and Affiliations

  1. Department of ECE, JNTUA, Anantapuramu, Andhra Pradesh, India

    K. R. Remya & M. N. Giriprasad

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  1. K. R. Remya
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  2. M. N. Giriprasad
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Remya K.R. and Giriprasad M.N. The first draft of the manuscript was written by Remya K.R. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to K. R. Remya.

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The data utilized in this research was obtained from publicly available benchmarked datasets

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Remya, K.R., Giriprasad, M.N. An automated exudate detection scheme supporting diabetic retinopathy screening using spatial-spectral-statistical feature maps. Multimed Tools Appl 81, 9829–9853 (2022). https://doi.org/10.1007/s11042-022-12354-9

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  • DOI: https://doi.org/10.1007/s11042-022-12354-9

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