Vessel Segmentation in Retinal Images Using Multi-scale Line Operator and K-Means Clustering

Area of Science:

• Ophthalmology
• Medical Image Analysis
• Computer Vision

Background:

• Accurate detection of retinal blood vessels is crucial for diagnosing various eye conditions.
• Lesions in retinal images, such as bright and dark spots, significantly complicate vessel segmentation.
• Existing methods often struggle with the presence of these artifacts, impacting diagnostic reliability.

Purpose of the Study:

• To develop and evaluate a robust method for retinal blood vessel detection in both normal and abnormal fundus images.
• To address the challenges posed by bright and dark lesions during vessel segmentation.
• To improve the accuracy and reliability of automated retinal image analysis.

Main Methods:

• A novel algorithm employing K-means segmentation in a perceptive color space to mitigate the influence of bright lesions.
• Utilization of a multi-scale line operator designed to detect linear vessel structures while distinguishing them from dark lesions.
• Validation of the proposed method on the publicly available STARE and DRIVE retinal image databases.

Main Results:

• The algorithm demonstrated high performance in vessel localization accuracy.
• Achieved a localization accuracy of 0.9483 on the STARE dataset.
• Achieved a localization accuracy of 0.9387 on the DRIVE dataset.

Conclusions:

• The proposed method effectively detects retinal blood vessels in the presence of challenging lesions.
• The algorithm shows promising results for automated analysis of retinal fundus images.
• This technique can enhance the diagnostic capabilities in ophthalmology by providing reliable vessel segmentation.