Fast Parabola Detection Using Estimation of Distribution Algorithms. (21st February 2017)

• Record Type: Journal Article
• Authors: Guerrero-Turrubiates, Jose de Jesus; Cruz-Aceves, Ivan; Ledesma, Sergio; Sierra-Hernandez, Juan Manuel; Velasco, Jonas; Avina-Cervantes, Juan Gabriel; Avila-Garcia, Maria Susana; Rostro-Gonzalez, Horacio; Rojas-Laguna, Roberto
• Other Names: Alonso-Betanzos Amparo Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2017(2017)
• Publication Date: 2017-02-21
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2017/6494390
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : 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.