Segmentation of Blood Vessels Using PCA and CNN

Abstract

Accurate segmentation of blood vessels in retinal images is a crucial task for diagnosing various eye diseases, including diabetic retinopathy, glaucoma, and macular degeneration. The complexity of retinal im- ages, characterized by varying illumination, noise, and low contrast, makes vessel segmentation challenging. This paper introduces a novel framework that integrates the feature extraction capabilities of Principal Component Analysis (PCA) with the segmentation power of Convo- lutional Neural Networks (CNNs). The proposed approach leverages PCA for dimensionality reduction and contrast enhancement, ensuring that essential features are retained while reducing computational complexity. CNNs are then employed to accurately segment blood vessels by learning spatial hierarchies and intricate vessel structures.

Extensive experiments are conducted on the DRIVE dataset, which includes a diverse set of retinal fundus im- ages with manually annotated vessel masks. The proposed PCA-CNN model demonstrates significant improvements in segmentation accuracy, precision, and recall compared to traditional segmentation techniques. The model achieves an accuracy of 98percent and an IoU score of 0.85, outper-forming existing methods. Furthermore, the integration of PCA and CNNs enhances computational efficiency, making the approach suitable for large-scale medical imaging applications.

By addressing the limitations of traditional segmentation methods, this work contributes to the advancement of automated retinal vessel segmentation. The insights gained from this study highlight the potential of combining di- mensionality reduction techniques with deep learning for enhanced medical image analysis, ultimately aiding in the early detection and diagnosis of retinal diseases.