Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation

Horng, Ming-Huwi

doi:10.1016/j.eswa.2011.04.180

Cited by 121 publications

(77 citation statements)

References 18 publications

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“…Colour is one of the most significant low-level feature that can be used to extract homogeneous regions which are related to objects or part of objects most of the time, multilevel thresholding technique approaches [50,51], thresholding approach in Otsu algorithm [52], threshold approach in segmentation of satellite images [53], and other applications [15,54]. …”

Section: Methodsmentioning

confidence: 99%

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Drulytė

Ruzgas

Raišutis

et al. 2018

Libyan Journal of Medicine

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Ultrasonic and digital dermatoscopy diagnostic methods are used in order to estimate the changes of structure, as well as to non-invasively measure the changes of parameters of lesions of human tissue. These days, it is very actual to perform the quantitative analysis of medical data, which allows to achieve the reliable early-stage diagnosis of lesions and help to save more lives. The proposed automatic statistical post-processing method based on integration of ultrasonic and digital dermatoscopy measurements is intended to estimate the parameters of malignant tumours, measure spatial dimensions (e.g. thickness) and shape, and perform faster diagnostics by increasing the accuracy of tumours differentiation. It leads to optimization of time-consuming analysis procedures of medical images and could be used as a reliable decision support tool in the field of dermatology.

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Section: Methodsmentioning

confidence: 99%

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Drulytė

Ruzgas

Raišutis

et al. 2018

Libyan Journal of Medicine

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“…Image segmentation is one of the most important steps in the image processing [1][2][3]. It is a process that divides a raw input image into a number of non-overlapping regions such that each region is homogeneous and the union of two adjoining regions is heterogeneous and extracts interested region from the others.…”

Section: Introductionmentioning

confidence: 99%

An Improved Image Segmentation Method based on Shannon Entropy and Biogeography based Optimization

Feng¹

2017

Proceedings of the 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016)

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Abstract. For the purpose of improve the effect of multilevel thresholding image segmentation, a new evolutionary optimization algorithm based on the science of biogeography for global optimization has been bring in namely Biogeography based optimization (BBO). In this paper we propose an improvement to BBO. In order to improve the diversity of population and to enhance its exploration ability, the Gaussian mutation operator is integrated into biogeography based optimization (BBO). And we combine this improved evolutionary algorithm and Shannon entropy to get multilevel thresholds of image segmentation. Experiments have been conducted on several images and compared with other algorithm namely ABC and DE.Simulation results and comparisons demonstrate the proposed BBO algorithm is better in terms of the quality of the solutions obtained.

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“…Similarly in [1,5], Particle Swarm Optimization (PSO) [13] has been proposed for MT proposes, maximizing the Otsu's function. Other examples such as [14][15][16] including Artificial Bee Colony (ABC) or Bacterial Foraging Algorithm (BFA) for image segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, if the fitness value for the best particle remains unspoiled in 10% of the total number of iterations ( max Iter ), then the MTHEMO is stopped. Such a criterion has been selected to maintain compatibility to similar works reported in the literature [14][15][16]. Table 1.…”

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confidence: 99%

“…On the other hand, the peak-to-signal ratio (PSNR) is used to compare the similarity of an image (image segmented) against a reference image (original image) based on the mean square error (MSE) of each pixel [3,14,33]. Both PSNR and MSE are defined as: Iter represents the stop criterion since the optimization point of view, in our experiments, the stop criterion is considered as the number of times in which the best fitness values remains with no change.…”

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confidence: 99%

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A Multilevel Thresholding algorithm using electromagnetism optimization

et al. 2014

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Segmentation is one of the most important tasks in image processing. It consist in classify the pixels into two or more groups depending on their intensity levels and a threshold value. The quality of the segmentation depends on the method applied to select the threshold. The use of the classical implementations for multilevel thresholding is computationally expensive since they exhaustively search the best values to optimize the objective function. Under such conditions, the use of optimization evolutionary approaches has been extended. The Electro-magnetism-Like algorithm (EMO) is an evolutionary method which mimics the attraction-repulsion mechanism among charges to evolve the members of a population. Different to other algorithms, EMO exhibits interesting search capabilities whereas maintains a low computational overhead. In this paper, a multilevel thresholding (MT) algorithm based on the EMO is introduced. The approach combines the good search capabilities of EMO algorithm with objective functions proposed by the popular MT methods of Otsu and Kapur. The algorithm takes random samples from a feasible search space inside the image histogram. Such samples build each particle in the EMO context whereas its quality is evaluated considering the objective that is function employed by the Otsu's or Kapur's method. Guided by these objective values the set of candidate solutions are evolved through the EMO operators until an optimal solution is found. The approach generates a multilevel segmentation algorithm which can effectively identify the threshold values of a digital image in a reduced number of iterations. Experimental results show performance evidence of the implementation of EMO for digital image segmentation.

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Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation

Cited by 121 publications

References 18 publications

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

An Improved Image Segmentation Method based on Shannon Entropy and Biogeography based Optimization

A Multilevel Thresholding algorithm using electromagnetism optimization

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