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No reference retinal image quality assessment using support vector machine classifier in wavelet domain

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Abstract

The automatic retinal screening system (ARSS) is a valuable computer-aided diagnosis tool for healthcare providers and public health initiatives. The ARSS facilitates mass retinal screenings that analyse retinal images and detect early signs of vision-threatening retinal diseases. The degradation in retinal image’s naturalness causes imprecise diagnosis. This paper proposed a quality assessment method that is suitable for ARSS and is important for closing care gaps and reducing healthcare costs in the field of healthcare. A no-reference (NR) quality assessment method utilizing natural scene statistics (NSS) and the multi-resolution approach is developed to detect retinal image quality. Image quality classification is performed combining NSS features and statistical featurenns of retinal image. A support vector machine classifier is used to map the retinal image features and find image quality. The proposed method is compared with existing NR image quality assessment methods. The results show that the proposed method has improved accuracy, recall, precision and F-measure values of 3.42%, 3.66%, 1.63% and 2.66%, respectively, over the competing methods, demonstrating its suitability for ARSS.

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Authors and Affiliations

  1. Department of ECE, Malla Reddy Engineering College (Autonomous), Maisammaguda, Hyderabad, Telangana, India

    Sima Sahu

  2. Department of Computer Science & Engineering, National Institute of Technology Patna, Patna, India

    Amit Kumar Singh

  3. AIIMS Bibinagar, Hyderabad, Telangana, India

    Nishita Priyadarshini

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  1. Sima Sahu
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  2. Amit Kumar Singh
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  3. Nishita Priyadarshini
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Correspondence to Amit Kumar Singh.

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Sahu, S., Singh, A. & Priyadarshini, N. No reference retinal image quality assessment using support vector machine classifier in wavelet domain. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19207-7

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