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<p>Diabetic retinopathy (DR) is a foremost cause of vision impairment, characterized by the presence of microaneurysms, exudates, and other intricate retinal lesions. Traditional image segmentation methods often encounter challenges with the complex features due to poor boundary detection, noise sensitivity, and difficulties with low-contrast images. These limitations result in suboptimal segmentation, potentially compromising accurate diagnosis. This study introduces a hybrid segmentation approach that integrates K-Means clustering with Graph Cut optimization. K-Means clustering provides an initial segmentation by categorizing the image into distinct regions, while Graph Cut refines these regions by optimizing boundary delineations based on pixel similarity and spatial continuity. This integration effectively overcomes the noise sensitivity and boundary detection issues of traditional methods, especially in low-contrast scenarios. The proposed method has been verified on standard datasets, achieving a Mean Squared Error (MSE) of 0.044, a Peak Signal-to-Noise Ratio (PSNR) of 40.84, and a Structural Similarity Index (SSIM) of 0.92. These results significantly surpass recent benchmarks reported in the literature, where existing methods typically achieve PSNR values between 30 and 35&#xa0;dB, MSE values between 0.038 ± 0.007 and SSIM values around 0.85 to 0.90. The improvements in PSNR and SSIM underscore the superior image quality and structural preservation offered by the approach. By merging the strengths of K-Means and Graph Cut, the proposed hybrid method provides a robust, scalable, and computationally efficient solution for retinal image segmentation, enhancing the early detection of DR and supporting global ophthalmic care initiatives. By leveraging this efficient algorithm, the proposed work promotes innovation in healthcare technology with focus on Sustainable Development Goal 9, ensuring accessible, accurate, and scalable solutions for ophthalmic care globally.</p>

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A novel approach for the segmentation of retinal images by integration K-means clustering algorithm with graph cut for image segmentation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月15日

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  • Ramasubramanian Bhoopalan,
  • S. Priyadharshini

摘要

Diabetic retinopathy (DR) is a foremost cause of vision impairment, characterized by the presence of microaneurysms, exudates, and other intricate retinal lesions. Traditional image segmentation methods often encounter challenges with the complex features due to poor boundary detection, noise sensitivity, and difficulties with low-contrast images. These limitations result in suboptimal segmentation, potentially compromising accurate diagnosis. This study introduces a hybrid segmentation approach that integrates K-Means clustering with Graph Cut optimization. K-Means clustering provides an initial segmentation by categorizing the image into distinct regions, while Graph Cut refines these regions by optimizing boundary delineations based on pixel similarity and spatial continuity. This integration effectively overcomes the noise sensitivity and boundary detection issues of traditional methods, especially in low-contrast scenarios. The proposed method has been verified on standard datasets, achieving a Mean Squared Error (MSE) of 0.044, a Peak Signal-to-Noise Ratio (PSNR) of 40.84, and a Structural Similarity Index (SSIM) of 0.92. These results significantly surpass recent benchmarks reported in the literature, where existing methods typically achieve PSNR values between 30 and 35 dB, MSE values between 0.038 ± 0.007 and SSIM values around 0.85 to 0.90. The improvements in PSNR and SSIM underscore the superior image quality and structural preservation offered by the approach. By merging the strengths of K-Means and Graph Cut, the proposed hybrid method provides a robust, scalable, and computationally efficient solution for retinal image segmentation, enhancing the early detection of DR and supporting global ophthalmic care initiatives. By leveraging this efficient algorithm, the proposed work promotes innovation in healthcare technology with focus on Sustainable Development Goal 9, ensuring accessible, accurate, and scalable solutions for ophthalmic care globally.