An efficient multi balanced cuckoo search K-means technique for segmentation and compression of compound images

Manju, V.; Fred, A. Lenin

doi:10.1007/s11042-018-6652-7

Cited by 8 publications

(6 citation statements)

References 13 publications

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“…Dhal et al used a modified bi-histogram along with CSA to find out the cancerous tissues from the mammography images [30]. Chakraborty et al used modified CSA for image segmentation of the hippocampus at the microscopic level [31], whereas Manju and Lenin Fred proposed the multi-balanced CSA using the K-means procedure for subdivision and compression of multipart images [32]. CSA has been also used for classification techniques in recent years.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 5(a) shows the input images of the brain, shoulder, spine, and knee. The corresponding enhanced images obtained are compared with the various existing algorithms namely, AGCWD [40], BBHE [51], DSIHE [52], DWT-SVD-AGC [32], DWT-SVD [3], HE [44], SHMS [53], WAHE [54], and Enhanced Cuckoo Search Algorithm (ECSA) [28] respectively. [40], (c) BBHE [51], (d) DSIHE [52], (e) DWT-SVD-AGC [32], (f) DWT-SVD [3], (g) HE [44], (h) SHMS [53], (i) WAHE [54], (j) ECSA [28] and (k)…”

Section: Performance Evaluation and Comparisonmentioning

confidence: 99%

“…Hence for better enhancement, we need the low value of GMSD. [40], (c) BBHE [51], (d) DSIHE [52], (e)DWT-SVD-AGC [32], (f) DWT-SVD [3], (g) HE [44], (h) SHMS [53], (i) WAHE [54], (j) ECSA [28] and (k) Proposed method . Quantitative comparisons of different method with proposed method (a) GMSD [47], (b) SSIM [46], (c) Discrete Entropy [48], (d) AMBE [48], (e) FSIM [46], (f) PCQI [49] and (g) MEME [48] The third column of Table 1 and Figure 9(b) depicts the variations and the nature of the SSIM quantitative index.…”

Section: Performance Evaluation and Comparisonmentioning

confidence: 99%

“…(a) Input images of various body parts. Enhanced output images using (b) AGCWD[40], (c) BBHE[51], (d) DSIHE[52], (e)DWT-SVD-AGC[32], (f) DWT-SVD[3], (g) HE[44], (h) SHMS[53], (i) WAHE[54], (j) ECSA[28] and (k) Proposed method…”

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confidence: 99%

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Cuckoo Search Constrained Gamma Masking for MRI Image Detail Enhancement

Ojha¹,

Rai²,

Prakash³

et al. 2022

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Nature-inspired algorithms are widely applied in the arena of image enhancement for various optimization purposes. To address the optimization complexities in various image enhancement approaches, nature-inspired optimization algorithms play a vital role. Cuckoo search is one of the prominent nature-inspired performance algorithms that we employed in this work for the enhancement of magnetic resonance imaging (MRI). We proposed a wavelet-based masking technique employing a cuckoo search algorithm whose masking value is corrected by gamma function for the contrast enhancement of MRI images. The cuckoo search algorithm can inevitably fine-tune the relation of nest building using genetic operatives like adaptive cusp and alteration. The proposed contrast enhancement scheme is examined quantitatively for different types of MRI images. Extensive simulation results compared with quantitative values have revealed that the traditional nest building of cuckoo search optimization is improved by adaptive gamma correction. Comparative analysis with the existing works establishes the usefulness of the proposed methodology over the other standard approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluation and Comparisonmentioning

confidence: 99%

Section: Performance Evaluation and Comparisonmentioning

confidence: 99%

mentioning

confidence: 99%

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Cuckoo Search Constrained Gamma Masking for MRI Image Detail Enhancement

Ojha¹,

Rai²,

Prakash³

et al. 2022

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“…Multiple kernel-based fuzzy c-means algorithm was hybridized with cuckoo search to produce MKF-cuckoo by Binu, Selvi, and George [119] with more effective objective functions designed by the researchers instead of using the K-means objective function. Manju and Fred [120] solved the problem of segmentation and compression of compound images using a hybrid of K-means clustering algorithm and multi-balanced cuckoo search algorithm. Deepa and Sumitra [121] combined cuckoo search optimization with a K-means clustering algorithm to achieve an optimal global solution in an intrusion detection system.…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

2021

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K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

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Cuckoo search constrained gamma masking for MRI image contrast enhancement

Prakash

Bhandari

2023

Multimed Tools Appl

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An efficient multi balanced cuckoo search K-means technique for segmentation and compression of compound images

Cited by 8 publications

References 13 publications

Cuckoo Search Constrained Gamma Masking for MRI Image Detail Enhancement

Cuckoo Search Constrained Gamma Masking for MRI Image Detail Enhancement

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

Cuckoo search constrained gamma masking for MRI image contrast enhancement

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