Analysis of Vasculature in Human Retinal Images Using Particle Swarm Optimization Based Tsallis Multi-level Thresholding and Similarity Measures

Raja, N. Sri Madhava; Kavitha, Ganesan; Swaminathan, Ramakrishnan

doi:10.1007/978-3-642-35380-2_45

Cited by 27 publications

(5 citation statements)

References 8 publications

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“…And the classification of this pixel is done by using Support Vector Machine based on their intensity level of damage in blood vessels. Nadaradjane Sri Madhava Raja et al [10] have used PSO based multilevel thresholding algorithm for the identification of tree like structures inside the retina and they extended their work by applying histogram equalization method during the pre-processing of the original image and then the segmentation process performed by applying Tsallis multilevel thresholding method and the result analysis of this method is done by using box plot representation it achieved an approximate mean value of 0.95% .…”

Section: Related Workmentioning

confidence: 99%

An Effective Segmentation of Retinal Blood Vessels using Optimized PCA and Morphological Operators

Kumar*¹,

Ramudu²,

srinivas³

et al. 2019

IJRTE

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The structure of blood vessels interior in the eye is a significant source of indicator for many diseases if exist in a human body. The extraction of vasculature network of blood vessels which plays a vital role in the study and diagnosis of many eye related diseases like diabetic retinopathy, glaucoma, and many cardiovascular diseases is a challenging task. In this paper, a new method to derive tree-shaped vasculature from retinal fundus RGB images is proposed. This proposed algorithm is performed in two stages: (1) Pre-processing stage involves Particle Swarm Optimization (PSO) algorithm to compute optimized image which holds the global optimal pixels of the input RGB image followed by conversion of PSO optimized image to gray image using PCA which is then contrast enhanced with CLAHE. (2) Post-processing stage is carried out working on the contrast enhanced gray image for attaining better accuracy of retinal blood vessel segmentation by using Thresholding as well as morphological operator. The performance measures of proposed method are evaluated on DRIVE and STARE databases and obtained best results with an average accuracy of 96.44% and proven to be an outstanding method compared to other existing retina vessel segmentation algorithms.

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Section: Related Workmentioning

confidence: 99%

An Effective Segmentation of Retinal Blood Vessels using Optimized PCA and Morphological Operators

Kumar*¹,

Ramudu²,

srinivas³

et al. 2019

IJRTE

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show abstract

“…Recent literature illustrates that the heuristic and metaheuristic algorithms such as particle swarm optimization (PSO) [20][21][22][23][24][25], bacterial foraging algorithm (BFO) [1,13,17,18], differential evaluation (DE) [19,[26][27][28], artificial bee colony (ABC) [11,29], cuckoo search (CS) [12,30], watershed algorithm [31], fuzzy logic [32], hybrid method [33], and self-adaptive parameter optimization algorithm [34] are widely considered for optimal multilevel image segmentation problem to enhance the outcome.…”

Section: Modelling and Simulation In Engineeringmentioning

confidence: 99%

“…The chief parameters which decide the efficiency of the FA are the variations of light intensity and attractiveness between neighboring fireflies. These two parameters will be affected by the increase in the distance between fireflies [23].…”

Section: Fundamentalsmentioning

confidence: 99%

Otsu Based Optimal Multilevel Image Thresholding Using Firefly Algorithm

Raja

Rajinikanth

Latha

2014

Modelling and Simulation in Engineering

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Histogram based multilevel thresholding approach is proposed using Brownian distribution (BD) guided firefly algorithm (FA). A bounded search technique is also presented to improve the optimization accuracy with lesser search iterations. Otsu's betweenclass variance function is maximized to obtain optimal threshold level for gray scale images. The performances of the proposed algorithm are demonstrated by considering twelve benchmark images and are compared with the existing FA algorithms such as Lévy flight (LF) guided FA and random operator guided FA. The performance assessment comparison between the proposed and existing firefly algorithms is carried using prevailing parameters such as objective function, standard deviation, peak-to-signal ratio (PSNR), structural similarity (SSIM) index, and search time of CPU. The results show that BD guided FA provides better objective function, PSNR, and SSIM, whereas LF based FA provides faster convergence with relatively lower CPU time.

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“…Tsalis entropy is a non-extensive statistical approach [21][22][23], being typically adopted in image processing applications [24][25][26][27][28][29][30][31]. The combination of Tsallis function and heuristic algorithms, such as particle swarm optimization (PSO) [24][25][26], differential evolution [27][28][29], bacterial foraging [30], cuckoo search [31], and artificial bee colony [32,33], have been presented over the past years. Also Fractional-order Darwinian PSO (FDPSO) is use to segment the grayscale images with Tsallis function.…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Thresholding with Fractional-Order Darwinian PSO and Tsallis Function

Pugalenthi¹,

Oliver²

2019

IJITEE

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A novel optimal multi-level thresholding is proposed using gray scale images for Fractional-order Darwinian Particle Swarm Optimization (FDPSO) and Tsallis function. The maximization of Tsallis entropy is chosen as the Objective Function (OF) which monitors FDPSO’s exploration until the search converges to an optimal solution. The proposed method is tested on six standard test images and compared with heuristic methods, such as Bat Algorithm (BA) and Firefly Algorithm (FA). The robustness of the proposed thresholding procedure was tested and validated on the considered image data set with Poisson Noise (PN) and Gaussian Noise (GN). The results obtained with this study verify that, FDPSO offers better image quality measures when compared with BA and FA algorithms. Wilcoxon’s test was performed by Mean Structural Similarity Index (MSSIM), and the results prove that image segmentation is clear even in noisy dataset based on the statistical significance of the FDPSO with respect to BA and FA.

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Analysis of Vasculature in Human Retinal Images Using Particle Swarm Optimization Based Tsallis Multi-level Thresholding and Similarity Measures

Cited by 27 publications

References 8 publications

An Effective Segmentation of Retinal Blood Vessels using Optimized PCA and Morphological Operators

An Effective Segmentation of Retinal Blood Vessels using Optimized PCA and Morphological Operators

Otsu Based Optimal Multilevel Image Thresholding Using Firefly Algorithm

Multi-Level Thresholding with Fractional-Order Darwinian PSO and Tsallis Function

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