Circle detection using discrete differential evolution optimization

Abstract

Objective: To measure levels of soluble tumour necrosis factor alpha (TNF) receptor type I (sTNFRI) and type II (sTNFRII) in order to correlate them with C-reactive protein (CRP), rheumatoid factor (RF), erythrocyte sedimentation rate (ESR), and disease activity score (DAS28) in RA patients. Methods: We recruited 41 RA patients classified according to American College of Rheumatology (ACR) criteria and 38 healthy subjects (HS). sTNFRI and sTNFRII were measured using an enzyme-linked immunosorbent assay (ELISA) kit. Clinical activity in RA patients was evaluated using the Disease Activity Score using 28 joint counts (DAS28). The statistical analysis was realized using SPSS version 10.0. Results: Soluble TNFRI and TNFRII levels were higher in RA patients (p = 0.04 and 0.001, respectively) than HS. Serum levels of sTNFRI correlated with sTNFRII (r = 0.699, p < 0.0001). sTNFRII correlated with DAS28 (r = 0.375, p = 0.017), RF (r = 0.505, p = 0.004), and ESR (r = 0.323, p = 0.042). Conclusion: The increased levels of both sTNFRI and sTNFRII suggest a secondary event related to the inflammatory state observed in RA, whereas the correlation of sTNFRII with RF, ESR, and DAS28 reflects the preferential TNFRII shedding induced by TNF. sTNFRII may be useful as an additional inflammatory marker in RA. " 2009 Taylor & Francis on license from Scandinavian Rheumatology Research Foundation.",,,,,,"10.1080/03009740902865456",,,"http://hdl.handle.net/20.500.12104/40079","http://www.scopus.com/inward/record.url?eid=2-s2.0-70350075983&partnerID=40&md5=c34c0dbf00d98e2eaf5ac463e363750a

http://ovidsp.ovid.com/ovidweb.cgi?T=JS&CSC=Y&NEWS=N&PAGE=fulltext&D=med5&AN=19579138",,,,,,"5",,"Scandinavian Journal of Rheumatology",,"332

335",,"38",,"Scopus

WOS

MEDLINE",,,,"Index Medicus;Adult;Arthritis, Rheumatoid/bl [Blood];Biological Markers/bl [Blood];Blood Sedimentation;C-Reactive Protein/me [Metabolism];Case-Control Studies;Enzyme-Linked Immunosorbent Assay;Female;Humans;Male;Middle Aged;Receptors, Tumor Necrosis Factor, Type I/bl [Blood];Receptors, Tumor Necrosis Factor, Type II/bl [Blood];Rheumatoid Factor/bl [Blood];Severity of Illness Index",,,,,,,,"Circulating TNFRI and TNFRII levels correlated with the disease activity score (DAS28) in rheumatoid arthritis",,"Article" "41853","123456789/35008",,"Cuevas, E., Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, Mexico; Zaldivar, D., Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, Mexico; Pérez-Cisneros, M., Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, Mexico; Ramírez-Ortegón, M., Freie Universityt Berlin, Takustrs 9, 14195 Berlin, Germany",,"Cuevas, E.

Zaldivar, D.

Perez-Cisneros, M.

Ramirez-Ortegon, M.",,"2011",,"This paper introduces a circle detection method based on differential evolution (DE) optimization. Just as circle detection has been lately considered as a fundamental component for many computer vision algorithms, DE has evolved as a successful heuristic method for solving complex optimization problems, still keeping a simple structure and an easy implementation. It has also shown advantageous convergence properties and remarkable robustness. The detection process is considered similar to a combinational optimization problem. The algorithm uses the combination of three edge points as parameters to determine circle candidates in the scene yielding a reduction of the search space. The objective function determines if some circle candidates are actually present in the image. This paper focuses particularly on one DE-based algorithm known as the discrete differential evolution (DDE), which eventually has shown better results than the original DE in particular for solving combinatorial problems. In the DDE, suitable conversion routines are incorporated into the DE, aiming to operate from integer values to real values and then getting integer values back, following the crossover operation. The final algorithm is a fast circle detector that locates circles with sub-pixel accuracy even considering complicated conditions and noisy images. Experimental results on several synthetic and natural images with varying range of complexity validate the efficiency of the proposed technique considering accuracy, speed, and robustness. " 2010 Springer-Verlag London Limited.