A new technique for multilevel color image thresholding based on modified fuzzy entropy and Lévy flight firefly algorithm

Pare, S.; Bhandari, Ashish Kumar; Kumar, Anil; Singh, Girish Kumar

doi:10.1016/j.compeleceng.2017.08.008

Cited by 74 publications

(31 citation statements)

References 25 publications

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“…Let two thresholds T1 and T2 partition the image into three distinguished regions D1, D2 and D3 and its corresponding probability distribution function P1, P2, P3 membership function µ1, µ2, µ3 and each region fuzzy entropy is H1, H2, H3 respectively then the overall fuzzy entropy is = 1 + 2 + 3 (2) After evaluation of overall fuzzy entropy, optimal thresholds are finding by using eq. (15) as in reference [21]…”

Section: Fuzzy Entropymentioning

confidence: 99%

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Optimal Threshold Based Brain Image Fusion for Brain Cancer Detection using Firefly algorithm

Srikanth¹,

Prasad²,

Prasad³

2019

IJRTE

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In this paper an attempt is made to diagnose brain disease like neoplastic disease, cerebrovascular disease, Alzheimer disease, fatal disease, Sarcoma disease by effective fusion of two images. Two images are fused in three steps: Step 1.Segmentation: The images are segmented on the basis of optimal thresholding; thresholds are optimized with natural inspired firefly algorithm by assuming fuzzy entropy as objective function. Image thresholding is one of the segmentation techniques which is flexible, simple and has less convergence time as compared to others. Step 2: the segmented features are extracted with Scale Invariant Feature Transform (SIFT) algorithm. The SIFT algorithm is good in extracting the features even after image rotation and scaling. Step 3: Finally fusion rules are made on the basis of interval type-2 fuzzy (IT2FL), where uncertainty effects are minimized unlike type-1. The novelty of the proposed work is tested on different benchmark Image fusion data set and proved better in all measuring parameters.

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Section: Fuzzy Entropymentioning

confidence: 99%

“…The entropy can be known if probability density (p) of pixel levels is known. If the entropy is higher, then fusion is efficient [21].…”

Section: Mutual Information (Mi)mentioning

confidence: 99%

Optimal Threshold Based Brain Image Fusion for Brain Cancer Detection using Firefly algorithm

Srikanth¹,

Prasad²,

Prasad³

2019

IJRTE

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“…The results of the comparison experiment show that the well-delimited regions obtained are easier to distinguish than other techniques [17]. Furthermore, some other algorithms or their modified versions have also been introduced into this domain, such as whale optimization algorithm (WOA) [18], multi-verse optimizer (MVO) [19], grasshopper optimization algorithm (GOA) [20], social spiders optimization (SSO) [21], krill herd optimization (KHO) [22], and cuckoo search (CS) [23] as well as Lévy flight firefly algorithm (LFA) [24], hybrid differential evolution (hjDE) [25], adaptive wind driven optimization (AWDO) [26], etc. These promising results motivate us to apply some other efficient meta-heuristic algorithms to multilevel image thresholding.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation

et al. 2019

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In this paper, a novel satellite image segmentation technique based on dynamic Harris hawks optimization with a mutation mechanism (DHHO/M) is proposed. Compared with the original Harris hawks optimization (HHO), the dynamic control parameter strategy and mutation operator used in DHHO/M can avoid falling into the local optimum and efficiently enhance the search capability. To evaluate the performance of the proposed method, a series of experiments are carried out on various satellite images. Eight advanced thresholding approaches are selected for comparison. Three criteria are adopted to determine the segmentation thresholds, namely Kapur’s entropy, Tsallis entropy, and Otsu between-class variance. Furthermore, four oil pollution images are used to further assess the practicality and feasibility of the proposed method on real engineering problem. The experimental results illustrate that the DHHO/M based thresholding technique is superior to others in the following three aspects: fitness function evaluation, image segmentation effect, and statistical tests.

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“…Bi-level thresholding methods involve one threshold value which partitions the image into two classes: foreground and background, however if the image is quite complex and contains various objects, the bi-level thresholding method is not very effective [27][28][29][30]. Therefore, multilevel thresholding methods are used extensively for image segmentation [31][32][33]. In this paper, a famous multilevel thresholding technique is used to determine the threshold values, namely, Kapur's entropy.…”

mentioning

confidence: 99%

Kapur’s Entropy for Color Image Segmentation Based on a Hybrid Whale Optimization Algorithm

Lang

Jia

2019

Entropy

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In this paper, a new hybrid whale optimization algorithm (WOA) called WOA-DE is proposed to better balance the exploitation and exploration phases of optimization. Differential evolution (DE) is adopted as a local search strategy with the purpose of enhancing exploitation capability. The WOA-DE algorithm is then utilized to solve the problem of multilevel color image segmentation that can be considered as a challenging optimization task. Kapur’s entropy is used to obtain an efficient image segmentation method. In order to evaluate the performance of proposed algorithm, different images are selected for experiments, including natural images, satellite images and magnetic resonance (MR) images. The experimental results are compared with state-of-the-art meta-heuristic algorithms as well as conventional approaches. Several performance measures have been used such as average fitness values, standard deviation (STD), peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), Wilcoxon’s rank sum test, and Friedman test. The experimental results indicate that the WOA-DE algorithm is superior to the other meta-heuristic algorithms. In addition, to show the effectiveness of the proposed technique, the Otsu method is used for comparison.

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A new technique for multilevel color image thresholding based on modified fuzzy entropy and Lévy flight firefly algorithm

Cited by 74 publications

References 25 publications

Optimal Threshold Based Brain Image Fusion for Brain Cancer Detection using Firefly algorithm

Optimal Threshold Based Brain Image Fusion for Brain Cancer Detection using Firefly algorithm

Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation

Kapur’s Entropy for Color Image Segmentation Based on a Hybrid Whale Optimization Algorithm

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