A Scale Normalized Generalized LoG Detector Approach for Retinal Vessel Segmentation

• Published in: IEEE access Vol. 9; pp. 44442 - 44452
• Main Authors: Khan, Mohammad A. U., Abdullah, Faizan, Akram, Awais, Naqvi, Rizwan Ali, Mehmood, Mehwish, Hussain, Dildar, Soomro, Toufique Ahmed
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blob detection; Blood vessels; Computer vision; Detectors; Edge detection; Image analysis; Image processing; Image segmentation; Laplacian of Gaussian; Medical imaging; Performance evaluation; Retinal images; retinal vessel extraction; Retinal vessels; ridge detection; Sensitivity; Sensors; Shape; Smoothing; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3063292

Retinal vessel segmentation is important for analyzing many retinal diseases, where manual segmentation of these vessels is an extensive job. Automatic segmentation of these vessels can help much in the diagnosis of these retinal diseases. Several image processing schemes that are considering the retinal vessel segmentation are lacking in segmentation performance and robustness. Laplacian of Gaussian (LoG) detectors is a popular choice method for edge detection adapted for detecting circular blob detection. Laplacian kernel approximates the second-order derivative to the image, which can be effective in combination with Gaussian smoothing in the form of LoG. The LoG detectors are famous for good border detection in noisy images. Based on their parameter, their behavior can be switched between ridge or blob detection. However, their performance generally falls off for elliptical blobs. A generalized LoG detector was proposed recently to deal with elliptical blob detection. Comparing simulation studies for the second-order ridge detector with other popular ridge detectors provides evidence of its effectiveness. The proposed ridge detector shows promise when applied to detect vessels in real-world retinal images of a publicly available database. The method's capabilities are evaluated with a comparison of state of the art, and the performances are obtained on the database most used by researchers. The DRIVE,STARE and CHASE_DB1 databases are used for performance evaluation, and we achieved a sensitivity of 0.785, a specificity of 0.967 and an accuracy of 0.952 on the DRIVE database,a sensitivity of 0.788, a specificity of 0.966 and a precision of 0.951 and a sensitivity of 0.787, a specificity of 0.968 and a precision of 0.952 on CHASE_DB1.