Localisation and segmentation of optic disc with the fractional‐order Darwinian particle swarm optimisation algorithm

Guo, Fan; Peng, Hui; Zou, Beiji; Zhao, Rongchang; Liu, Xiyao

doi:10.1049/iet-ipr.2017.1149

Cited by 24 publications

(13 citation statements)

References 33 publications

(67 reference statements)

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“…However, other performance metrics like S and SEN of proposed algorithm beats the results of MaxVess algorithm in [10]. For the ORIGA data set, the proposed algorithm results in higher accuracy than the FODPSO algorithm [13] because the proposed algorithm is robust to the exudates in the retinal images. In case of DRIVE and DIARETDB1 data sets, the proposed algorithm results in higher performance than the method based on the vascular structure using the watershed transform in [11].…”

Section: Experiments and Resultsmentioning

confidence: 95%

“…Feature‐based methods basically rely on the characteristics of the OD like brightness, shape, and size. Guo et al [13] proposed segmentation based on the fractional‐order Darwinian particle swarm optimisation (FODPSO) algorithm to segment the OD based on intensity. FODPSO group the input image into regions having the same intensity followed by the ellipse fitting to detect the elliptical OD region.…”

Section: Overview Of Prior Workmentioning

confidence: 99%

See 1 more Smart Citation

Robust retinal optic disc and optic cup segmentation via stationary wavelet transform and maximum vessel pixel sum

Biswal¹,

Eadara²,

Sairam³

et al. 2020

IET Image Processing

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Section: Experiments and Resultsmentioning

confidence: 95%

Section: Overview Of Prior Workmentioning

confidence: 99%

Robust retinal optic disc and optic cup segmentation via stationary wavelet transform and maximum vessel pixel sum

Biswal¹,

Eadara²,

Sairam³

et al. 2020

IET Image Processing

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“…The reason for distinguishing this category is that the fractional-order derivative is not applied to the image but to a parameter of the model at hand such as the optimal threshold. The main application in this category is image segmentation [70]- [72].…”

Section: Related Work 1) Fractional Derivative Operators In Imagementioning

confidence: 99%

Discrete Laplacian Operator and Its Applications in Signal Processing

2020

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Fractional calculus has increased in popularity in recent years, as the number of its applications in different fields has increased. Compared to the traditional operations in calculus (integration and differentiation) which are uniquely defined, the fractional-order operators have numerous definitions. Furthermore, a consensus on the most suitable definition for a given task is yet to be reached. Fractional operators are defined as continuous operators and their implementation requires a discretization step. In this article, we propose a discrete fractional Laplacian as a matrix operator. The proposed operator is real (non-complex) which makes it computationally efficient. The construction of the proposed fractional Laplacian utilizes the DCT transform avoiding the complexity associated with the discretization step which is typical in the constructions based on signal processing. We demonstrate the utility of the proposed operator on a number of data modeling and image processing tasks. INDEX TERMS Fractional-Laplacian, discrete operator, image-processing, trend-filtering, fractional calculus.

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“…Gao et al [36], Couceiro and Ghamisi [37], Guo et al [38], and Hosseini et al [39] also applied a fraction-order (FO) in PSO and Darwinian PSO (DPSO). However, the most recent studies in FPSO have talked about the applications, such as the direction of arrival estimation for electromagnetic plane waves [40], fractional order filters design [41], fractional fixed-structure H ∞ controller design [42], image segmentation [43][44][45][46], image border detection [47], design of a complementary metal-oxide-semiconductor (CMOS) power amplifier [39], design for an electric power transmission system [48], optimization for a pressurized water reactor (PWR) core loading pattern [49], and optimization of extreme learning machine assignments [50]. This paper proposes an FPSO algorithm with a non-linear time-varying evolution (NTE) based on Ko et al [16] and Solteiro Pires et al [34].…”

Section: Introductionmentioning

confidence: 99%

Adaptive-Uniform-Experimental-Design-Based Fractional-Order Particle Swarm Optimizer with Non-Linear Time-Varying Evolution

et al. 2019

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An adaptive-uniform-experimental-design-based fractional particle swarm optimizer (AUFPSO) with non-linear time-varying evolution (NTE) is proposed. A particle swarm optimizer (PSO) is an excellent evolutionary algorithm due to its simple structure and rapid convergence. Nevertheless, PSO has notable drawbacks. Although many proposed methods and strategies have enhanced its effectiveness and performance, PSO is limited by its tendency to fall into local optima and its tendency to obtain different solutions in each search (i.e., its weak robustness). Introducing fractional-order calculus in PSO (FPSO) can correct the order of the velocity derivative for each particle, which enhances the diversity and algorithmic effectiveness. This study used NTE of the order of the velocity derivative, inertia weight, cognitive parameter, and social parameter in an FPSO used to search for a global optimal solution. To obtain the best combination of FPSO and NTE, an adaptive uniform experimental design (AUED) method was used to deal with this essential issue. The AUED method integrates a uniform layout with the best combination phase and a stepwise ratio to assist in selecting the best combination for FPSO-NTE. Experimental applications in 15 global numerical optimization problems confirmed that the AUFPSO-NTE had a better performance and robustness than existing PSO-related algorithms.

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Localisation and segmentation of optic disc with the fractional‐order Darwinian particle swarm optimisation algorithm

Cited by 24 publications

References 33 publications

Robust retinal optic disc and optic cup segmentation via stationary wavelet transform and maximum vessel pixel sum

Robust retinal optic disc and optic cup segmentation via stationary wavelet transform and maximum vessel pixel sum

Discrete Laplacian Operator and Its Applications in Signal Processing

Adaptive-Uniform-Experimental-Design-Based Fractional-Order Particle Swarm Optimizer with Non-Linear Time-Varying Evolution

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