TY - JOUR
T1 - Fast parabola detection using estimation of distribution algorithms
AU - Guerrero-Turrubiates, Jose De Jesus
AU - Cruz-Aceves, Ivan
AU - Ledesma, Sergio
AU - Sierra-Hernandez, Juan Manuel
AU - Velasco, Jonas
AU - Avina-Cervantes, Juan Gabriel
AU - Avila-Garcia, Maria Susana
AU - Rostro-Gonzalez, Horacio
AU - Rojas-Laguna, Roberto
N1 - Publisher Copyright:
Copyright © 2017 Jose de Jesus Guerrero-Turrubiates et al.
PY - 2017
Y1 - 2017
N2 - This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications.
AB - This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications.
KW - Circle detection
KW - Hough transform
KW - Image-analysis
UR - https://www.scopus.com/pages/publications/85019726533
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=pure_univeritat_ramon_llull&SrcAuth=WosAPI&KeyUT=WOS:000396248300001&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.1155/2017/6494390
DO - 10.1155/2017/6494390
M3 - Article
C2 - 28321264
AN - SCOPUS:85019726533
SN - 1748-670X
VL - 2017
JO - Computational and Mathematical Methods in Medicine
JF - Computational and Mathematical Methods in Medicine
M1 - 6494390
ER -