Entropy-based imagery segmentation for breast histology using the Stochastic Fractal Search

Hinojosa, Salvador; Dhal, Krishna Gopal; Elaziz, Mohamed E. Abd; Oliva, Diego; Cuevas, Erik

doi:10.1016/j.neucom.2018.09.034

Cited by 53 publications

(17 citation statements)

References 41 publications

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“…where rand is a uniformly distributed random number in the interval [0,1]. CR is a constant that indicates the crossover probability.…”

Section: Crossovermentioning

confidence: 99%

“…Image segmentation is one of the most important techniques in image processing, which partitions a given image into several unique and disjoint classes according to color, texture, edge, and other parameters [1][2][3][4][5]. In the last few decades, many segmentation methods have been proposed by researchers, such as clustering, edge detection, region growing, and thresholding [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation

et al. 2019

Self Cite

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An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA–jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search efficiency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon’s rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method.

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“…where rand is a uniformly distributed random number in the interval [0,1]. CR is a constant that indicates the crossover probability.…”

Section: Crossovermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation

et al. 2019

Self Cite

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“…In the image, the uniform region corresponds to the minimum entropy, while the non-uniform region defines the maximum entropy. Therefore, a better segmentation effect can be obtained by obtaining a larger Boltzmann-Gibbs entropy of the segmented image [13,14]. Therefore, entropy-based algorithms with different characteristics are well known.…”

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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“…Chowdhury et al used Shannon's entropy and proposed a new multithreshold image segmentation method based on minimization of bientropy function [20]. Hinojosa et al used three of the most representative entropies-Kapur, minimum cross entropy, and Tsallis as objective functions [21]. Mishra et al calculated the optimal threshold values using bat algorithm and maximizing different objective function values based on Kapur's entropy [22].…”

Section: Introductionmentioning

confidence: 99%

An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

Wang

et al. 2019

Mathematical Problems in Engineering

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In order to realize the multithreshold segmentation of images, an improved segmentation algorithm based on graph cut theory using artificial bee colony is proposed. A new weight function based on gray level and the location of pixels is constructed in this paper to calculate the probability that each pixel belongs to the same region. On this basis, a new cost function is reconstructed that can use both square and nonsquare images. Then the optimal threshold of the image is obtained through searching for the minimum value of the cost function using artificial bee colony algorithm. In this paper, public dataset for segmentation and widely used images were measured separately. Experimental results show that the algorithm proposed in this paper can achieve larger Information Entropy (IE), higher Peak Signal to Noise Ratio (PSNR), higher Structural Similarity Index (SSIM), smaller Root Mean Squared Error (RMSE), and shorter time than other image segmentation algorithms.

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Entropy-based imagery segmentation for breast histology using the Stochastic Fractal Search

Cited by 53 publications

References 41 publications

Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation

Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation

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

An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

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