Optimizing the trainable B-COSFIRE filter for retinal blood vessel segmentation

PeerJ

article

Sufian A. Badawi¹  Muhammad Moazam Fraz¹ 

[1] School of Electrical Engineering and Computer Science, National University of Sciences and Technology

关键词: Retinal blood vessels;    B-COSFIRE;    Retinal images;    Computer Aided Diagnosis (CAD);    BCOSFIRE;   

DOI  :  10.7717/peerj.5855

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Segmentation of the retinal blood vessels using filtering techniques is a widely used step in the development of an automated system for diagnostic retinal image analysis. This paper optimized the blood vessel segmentation, by extending the trainable B-COSFIRE filter via identification of more optimal parameters. The filter parameters are introduced using an optimization procedure to three public datasets (STARE, DRIVE, and CHASE-DB1). The suggested approach considers analyzing thresholding parameters selection followed by application of background artifacts removal techniques. The approach results are better than the other state of the art methods used for vessel segmentation. ANOVA analysis technique is also used to identify the most significant parameters that are impacting the performance results (p-value ¡ 0.05). The proposed enhancement has improved the vessel segmentation accuracy in DRIVE, STARE and CHASE-DB1 to 95.47, 95.30 and 95.30, respectively.

【 授权许可】

CC BY