Circle detection using discrete differential evolution optimization

Cuevas, Erik; Zaldívar, Daniel; Pérez‐Cisneros, Marco; Ramírez-Ortegón, Marte A.

doi:10.1007/s10044-010-0183-9

Cited by 56 publications

(31 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Sub-voxel resolution utilizes local voxel intensity data to interpolate image data. Sub-voxel imaging techniques are valuable tools that can improve image registration and have been commonly employed in computer vision (Bing et al, 2009; Cuevas et al, 2011) and in biomedical research (Nakamura et al, 2008). The high contrast amongst soft tissues in our scans allowed for excellent sub-voxel interpolation.…”

Section: Discussionmentioning

confidence: 99%

Whole-globe biomechanics using high-field MRI

Voorhees

Jan

et al. 2017

Experimental Eye Research

View full text Add to dashboard Cite

The eye is a complex structure composed of several interconnected tissues acting together, across the whole globe, to resist deformation due to intraocular pressure (IOP). However, most work in the ocular biomechanics field only examines the response to IOP over smaller regions of the eye. We used high-field MRI to measure IOP induced ocular displacements and deformations over the whole globe. Seven sheep eyes were obtained from a local abattoir and imaged within 48 hours using MRI at multiple levels of IOP. IOP was controlled with a gravity perfusion system and a cannula inserted into the anterior chamber. T2-weighted imaging was performed to the eyes serially at 0 mmHg, 10 mmHg, 20 mmHg and 40 mmHg of IOP using a 9.4 Tesla MRI scanner. Manual morphometry was conducted using 3D visualization software to quantify IOP-induced effects at the globe scale (e.g. axial length and equatorial diameters) or optic nerve head scale (e.g. canal diameter, peripapillary sclera bowing). Measurement sensitivity analysis was conducted to determine measurement precision. High-field MRI revealed an outward bowing of the posterior sclera and anterior bulging of the cornea due to IOP elevation. Increments in IOP from 10 to 40 mmHg caused measurable increases in axial length in 6 of 7 eyes of 7.9±5.7% (mean±SD). Changes in equatorial diameter were minimal, 0.4±1.2% between 10 and 40 mmHg, and in all cases less than the measurement sensitivity. The effects were nonlinear, with larger deformations at normal IOPs (10–20mmHg) than at elevated IOPs (20–40mmHg). IOP also caused measurable increases in the nasal-temporal scleral canal diameter of 13.4±9.7% between 0 and 20 mmHg, but not in the superior-inferior diameter. This study demonstrates that high-field MRI can be used to visualize and measure simultaneously the effects of IOP over the whole globe, including the effects on axial length and equatorial diameter, posterior sclera displacement and bowing, and even changes in scleral canal diameter. The fact that the equatorial diameter did not change with IOP, in agreement with previous studies, indicates that a fixed boundary condition is a reasonable assumption for half globe inflation tests and computational models. Our results demonstrate the potential of high-field MRI to contribute to understanding ocular biomechanics, and specifically of the effects of IOP in large animal models.

show abstract

Section: Discussionmentioning

confidence: 99%

Whole-globe biomechanics using high-field MRI

Voorhees

Jan

et al. 2017

Experimental Eye Research

View full text Add to dashboard Cite

show abstract

“…Examples include genetic algorithm based approaches [8], differential evolution based approaches [17], and bacterial foraging algorithm based approaches [10]. These methods all have the common feature that they turn the circle detection problem into a mathematical optimisation problem.…”

Section: Circle Detection Using Metaheuristicsmentioning

confidence: 99%

Robust Circle Detection Using Harmony Search

Fourie

2017

Journal of Optimization

View full text Add to dashboard Cite

Automatic circle detection is an important element of many image processing algorithms. Traditionally the Hough transform has been used to find circular objects in images but more modern approaches that make use of heuristic optimisation techniques have been developed. These are often used in large complex images where the presence of noise or limited computational resources make the Hough transform impractical. Previous research on the use of the Harmony Search (HS) in circle detection showed that HS is an attractive alternative to many of the modern circle detectors based on heuristic optimisers like genetic algorithms and simulated annealing. We propose improvements to this work that enables our algorithm to robustly find multiple circles in larger data sets and still work on realistic images that are heavily corrupted by noisy edges.

show abstract

“…The big spatial data is analyzed in research areas like robotics, pattern recognition, and/or computer vision. In most of these areas, good results have been achieved with the DE (see, e.g., [1, 4–7]). This paper proposes a novel DE based algorithm solving the combinatorial tasks with discrete vertices.…”

Section: Introductionmentioning

confidence: 99%

“…The Differential Evolution is often used for the pattern recognition [7, 36], clustering [37], classification, or feature extraction [38]. All these disciplines find utilization in the bioinspired systems and robot automation [4, 5, 22] or computer vision [36, 39].…”

Section: Introductionmentioning

confidence: 99%

“…The article [38] summarizes different applications of evolutionary algorithms in the pattern recognition and machine learning including the Differential Evolution. The DE has been utilized for human body pose estimation from the point clouds [6, 36, 40], circles detection [7], ellipses detection [41], recognition of leukocytes in images, or 3D face model reconstruction utilizing multiview 2D images [42]. Most of the referenced algorithms optimize analytically a temporary pattern shape, deformable or active shape models.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Utilization of the Discrete Differential Evolution for Optimization in Multidimensional Point Clouds

Uher

Gajdoš

Radecký

et al. 2016

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds.

show abstract

Circle detection using discrete differential evolution optimization

Cited by 56 publications

References 43 publications

Whole-globe biomechanics using high-field MRI

Whole-globe biomechanics using high-field MRI

Robust Circle Detection Using Harmony Search

Utilization of the Discrete Differential Evolution for Optimization in Multidimensional Point Clouds

Contact Info

Product

Resources

About