Parallel multiscale feature extraction and region growing: application in retinal blood vessel detection

Authors:
Miguel A. Palomera-Pérez;M. Elena Martinez-Perez;Hector Benítez-Pérez;Jorge Luis Ortega-Arjona
Affiliations:
Department of Computer Systems Engineering and Automatization, Instituto de Investigaciones en Matematicas Aplicadas y en Sistemas, Universidad Nacional Autónoma de Mexico, Mexico, Mexico;Department of Computer Science, Instituto de Investigaciones en Matematicas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Mexico, Mexico;Department of Computer Systems Engineering and Automatization, Instituto de Investigaciones en Matematicas Aplicadas y en Sistemas, Universidad Nacional Autónoma de Mexico, Mexico, Mexico;Department of Mathematics, Faculty of Science, Universidad Nacional Autonoma de México, Mexico, Mexico
Venue:
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Year:
2010

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Abstract

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.