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A Localized Feature Description Means Assisting Diabetic Macular Edema Detection and Classification

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Abstract

Heretofore schemes highly rely on complex feature characterization modules assisting the detection and classification of Diabetic Macular Edema (DME). DME encompasses intrinsic local variations that need to be analyzed for treatment. Accordingly, this paper delivers a localized fundus image feature characterization scheme supporting acute abnormality detection and classification of diverse DME abnormalities. This is accomplished by deploying the localized Triangulated Feature Descriptor (TFD) that solely operates on fundus images to capture appropriate features that are deemed essential for DME detection and classification. TFD is operated in three modes for extracting the vital features and is then coupled with simple image representation schemes to deliver precise feature descriptors concerned with diverse abnormalities. The resultant features are then classified by a supervised learning machine for grading the diverse DME ailments. The novel mechanism is elaborately investigated on benchmarked databases namely DRIVE, DIARETDB1, and MESSIDOR using Receiver Operating Characteristics parameters. The assessments reveal the superiority of the proposed approach over its counterparts. Also, the minimal computational operations involved in capitulating the essential image characteristics is another quality that makes this scheme amicable for real-time DR diagnosis.

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Data Availability

Three publicly available datasets namely DRIVE, 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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Funding

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. ECE Department, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India

    K. R. Remya

  2. ECE Department, JNTUACE, Anantapuramu, Andhra Pradesh, India

    M. N. Giriprasad

  3. School of Electronics Engineering, VIT, Vellore, India

    M. S. Sudhakar

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  1. K. R. Remya
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  2. M. N. Giriprasad
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  3. M. S. Sudhakar
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by KRR, MNG and MSS. The first draft of the manuscript was written by KER. 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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Remya, K.R., Giriprasad, M.N. & Sudhakar, M.S. A Localized Feature Description Means Assisting Diabetic Macular Edema Detection and Classification. Wireless Pers Commun 129, 2909–2927 (2023). https://doi.org/10.1007/s11277-023-10264-z

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