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

ABSTRACT: 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 integ

SUBMITTER: Bhoopalan R 

PROVIDER: S-EPMC12528709 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature