Improvements in retinal vessel clustering techniques: towards the automatic computation of the arterio venous ratio

• Published in: Computing Vol. 90; no. 3-4; pp. 197 - 217
• Main Authors: Vázquez, S. G., Barreira, N., Penedo, M. G., Ortega, M., Pose-Reino, A.
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01-11-2010; Springer; Springer Nature B.V
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Arteries; Arteriosclerosis; Artificial Intelligence; Blood vessels; Classification; Clustering; Computer Appl. in Administrative Data Processing; Computer Communication Networks; Computer Science; Computer science; control theory; systems; Diabetes; Diagnosis; Diseases; Exact sciences and technology; Hypertension; Information Systems Applications (incl.Internet); Labeling; Mathematical analysis; Mathematics; Medical diagnosis; Methods; Methods of scientific computing (including symbolic computation, algebraic computation); Numerical analysis; Numerical analysis. Scientific computation; Retina; Sciences and techniques of general use; Software Engineering; Strategy; Studies; Theoretical computing; Veins; Veins & arteries; Vessel clustering; 62H30; Arterio venous ratio; 68T10; 62H35; Numerical analysis; Scientific computation; Classification; Algorithm
• ISSN: 0010-485X; 1436-5057
• DOI: 10.1007/s00607-010-0114-z

Retinal blood vessel structure is an important indicator for diagnosis of several diseases such as diabetes, hypertension, arteriosclerosis, or stroke. These pathologies cause early alterations in the blood vessels that affect veins and arteries differently. In this sense, the arterio venous ratio is a measurement that evaluates these alterations and, consequently, the condition of the patient. Thus, a precise identification of both types of vessels is necessary in order to develop an automatic diagnosis system, to quantify the seriousness of disease, or to monitor the therapy. The classification of vessels into veins and arteries is difficult due to the inhomogeneity in the retinal image lightness and the similarity of both structures. In this paper, several image feature sets have been combined with three clustering strategies in order to find a suitable characterization methodology. The best strategy has managed to classify correctly the 86.34% of the vessels improving the results obtained with previous techniques.