Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentation

Naidu, M.S.R.; Kumar, Preetam; Chiranjeevi, K.

doi:10.1016/j.aej.2017.05.024

Cited by 92 publications

(37 citation statements)

References 24 publications

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“…The E d region covers the pixels with less grayscale value than T1, E m covers the pixels with a middle grayscale value between T1 and T2, and E b covers the pixels with more grayscale value than T2 . W a = E m , E b , and E d is the unknown probabilistic distribution of D domain, which probability distribution is described in [ 25 , 27 ].…”

Section: Methodsmentioning

confidence: 99%

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Thresholding for medical image segmentation for cancer using fuzzy entropy with level set algorithm

Maolood

Al-Salhi

2018

Open Medicine

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AbstractIn this study, an effective means for detecting cancer region through different types of medical image segmentation are presented and explained. We proposed a new method for cancer segmentation on the basis of fuzzy entropy with a level set (FELs) thresholding. The proposed method was successfully utilized to segment cancer images and then efficiently performed the segmentation of test ultrasound image, brain MRI, and dermoscopy image compared with algorithms proposed in previous studies. Results showed an excellent performance of the proposed method in detecting cancer image segmentation in terms of accuracy, precision, specificity, and sensitivity measures.

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Section: Methodsmentioning

confidence: 99%

“…The membership functions (μ) for E d , E m , and E b correspond to μ m , μ b , and μ d and requires six parameters for this process, that is, a1, b1, c1, a2, b2, and c2. accordance with the membership function (μ) the thresholds T1 and T2 are the variables for every pixel k = 0,1….,255 [ 26 , 27 ].…”

Section: Methodsmentioning

confidence: 99%

Thresholding for medical image segmentation for cancer using fuzzy entropy with level set algorithm

Maolood

Al-Salhi

2018

Open Medicine

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“…An improved fuzzy entropy and Lévy flying firefly algorithm (FA) method is used for color image threshold segmentation [21]. By maximizing Shannon entropy or fuzzy entropy, the FA is utilized to image segmentation [22]. On the other hand, in 2005, Masi et al proposed a newer and more coordinated Masi entropy which integrates the additivity of Renyi entropy and the non-extensibility of Tsallis entropy [23].…”

Section: Introductionmentioning

confidence: 99%

Masi Entropy for Satellite Color Image Segmentation Using Tournament-Based Lévy Multiverse Optimization Algorithm

et al. 2019

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A novel multilevel threshold segmentation method for color satellite images based on Masi entropy is proposed in this paper. Lévy multiverse optimization algorithm (LMVO) has a strong advantage over the traditional multiverse optimization algorithm (MVO) in finding the optimal solution for the segmentation in the three channels of an RGB image. As the work advancement introduces a Lévy multiverse optimization algorithm which uses tournament selection instead of roulette wheel selection, and updates some formulas in the algorithm with mutation factor. Then, the proposal is called TLMVO, and another advantage is that the population diversity of the algorithm in the latest iterations is maintained. The Masi entropy is used as an application and combined with the improved TLMVO algorithm for satellite color image segmentation. Masi entropy combines the additivity of Renyi entropy and the non-extensibility of Tsallis entropy. By increasing the number of thesholds, the quality of segmenttion becomes better, then the dimensionality of the problem also increases. Fitness function value, average CPU running time, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) were used to evaluate the segmentation results. Further statistical evaluation was given by Wilcoxon's rank sum test and Friedman test. The experimental results show that the TLMVO algorithm has wide adaptability to high-dimensional optimization problems, and has obvious advantages in objective function value, image quality detection, convergence performance and robustness.

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“…In [10], such results have been compared with wind driven optimization (WDO), ABC and PSO.Very encouraging results have been obtained by Naidu et al [11] is optimizing Tsallis entropy with ACS and modified the initial results obtained through CS in which step of work required to be made adaptive for attaining global maximum. They have also tried on similar lines on firefly algorithm [12]. This paper mainly focus on getting optimal thresholds for better segmentation that is achieved with a Hybrid combination of BFOA and PSO in the category of For the better understating and evaluation of presented proposed image thresholdingin 2-D histogram we deliberate Structural Similarity Index (SSIM), peak signal to noise ratio (PSNR), objective/fitness function and finally Misclassification error.…”

Section: Introductionmentioning

confidence: 99%

2-D histogram Based Multilevel Thresholding for Image Segmentation by Hybrid Bacterial Foraging Optimization and Particle Swarm Optimization

Kumar¹,

Reddy²,

Srinivas³

2019

IJITEE

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The objective of image segmentation is to extract meaningful clusters in given image. Meaningful clusters are possible with perfect threshold values which are optimized by assuming Renyi entropy as an objective function. A 1-D histogram based multilevel thresholding is computationally complex and segmented image visual quality comparatively low because of equal distribution of energy over the entire histogram plan. To overcome the problem, a 2-D histogram based multilevel thresholding is proposed in this paper by maximizing the Renyi entropy with a novel Hybrid Bacterial Foraging Optimization Algorithm and Particle Swarm Optimization (hBFOA-PSO) and the obtained results are compared with state of art optimization techniques. The results of the proposed model have been evaluated on a standard image dataset. The results obtained after implementing a 2-D histogram suggest hBFOA-PSO can be effectively used won multilevel thresholding problems resulting in a high accuracy.

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Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentation

Cited by 92 publications

References 24 publications

Thresholding for medical image segmentation for cancer using fuzzy entropy with level set algorithm

Thresholding for medical image segmentation for cancer using fuzzy entropy with level set algorithm

Masi Entropy for Satellite Color Image Segmentation Using Tournament-Based Lévy Multiverse Optimization Algorithm

2-D histogram Based Multilevel Thresholding for Image Segmentation by Hybrid Bacterial Foraging Optimization and Particle Swarm Optimization

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