Parallel Multiscale Feature Extraction and Region Growing: Application in Retinal Blood Vessel Detection

• Published in: IEEE transactions on information technology in biomedicine Vol. 14; no. 2; pp. 500 - 506
• Main Authors: Palomera-Perez, M.A., Martinez-Perez, M.E., Benitez-Perez, H., Ortega-Arjona, J.L.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-03-2010
• Subjects: Algorithms; Biomedical imaging; Blood vessels; Cardiovascular diseases; Data processing; Databases, Factual; Diabetes; Diagnostic Techniques, Ophthalmological; distributed algorithms; Feature extraction; Humans; Image analysis; Image databases; image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Iron; parallel programming; Reproducibility of Results; Retina; Retina - anatomy & histology; Retinal Vessels - anatomy & histology
• ISSN: 1089-7771; 1558-0032
• DOI: 10.1109/TITB.2009.2036604

This paper presents a parallel implementation based on insight segmentation and registration toolkit for a multiscale feature extraction and region growing algorithm, applied to retinal blood vessels segmentation. This implementation is capable of achieving an accuracy (Ac) comparable to its serial counterpart (about 92%), but 8 to 10 times faster. In this paper, the Ac of this parallel implementation is evaluated by comparison with expert manual segmentation (obtained from public databases). On the other hand, its performance is compared with previous published serial implementations. Both these characteristics make this parallel implementation feasible for the analysis of a larger amount of high-resolution retinal images, achieving a faster and high-quality segmentation of retinal blood vessels.