scholarly journals A New Harris Hawks-Cuckoo Search Optimizer for Multilevel Thresholding of Thermogram Images

2020 ◽  
Vol 34 (5) ◽  
pp. 541-551
Author(s):  
Leena Samantaray ◽  
Sabonam Hembram ◽  
Rutuparna Panda
Keyword(s):  
Performance Metrics ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Cuckoo Search ◽  
Function Optimization ◽  
Multilevel Thresholding ◽  
Infrared Images ◽  
Thresholding Method ◽  
Kapur’S Entropy ◽  
Feature Similarity Index

The exploitation capability of the Harris Hawks optimization (HHO) is limited. This problem is solved here by incorporating features of Cuckoo search (CS). This paper proposes a new algorithm called Harris hawks-cuckoo search (HHO-CS) algorithm. The algorithm is validated using 23 Benchmark functions. A statistical analysis is carried out. Convergence of the proposed algorithm is studied. Nonetheless, converting color breast thermogram images into grayscale for segmentation is not effective. To overcome the problem, we suggest an RGB colour component based multilevel thresholding method for breast cancer thermogram image analysis. Here, 8 different images from the Database for Research Mastology with Infrared images are considered for the experiments. Both 1D Otsu’s between-class variance and Kapur's entropy are considered for a fair comparison. Our proposal is evaluated using the performance metrics – Peak Signal to Noise Ratio (PSNR), Feature Similarity Index (FSIM), Structure Similarity Index (SSIM). The suggested method outperforms the grayscale based multilevel thresholding method proposed earlier. Moreover, our method using 1D Otsu’s fitness functions performs better than Kapur’s entropy based approach. The proposal would be useful for analysis of infrared images. Finally, the proposed HHO-CS algorithm may be useful for function optimization to solve real world engineering problems.

Elephant Herding Optimization for Multi-Level Image Thresholding

2020 ◽  
Vol 11 (4) ◽  
pp. 64-90
Author(s):  
Falguni Chakraborty ◽  
Provas Kumar Roy ◽  
Debashis Nandi
Keyword(s):  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Cuckoo Search ◽  
Threshold Value ◽  
Objective Functions ◽  
Image Thresholding ◽  
Bee Colony ◽  
Kapur’S Entropy ◽  
Feature Similarity Index

Multilevel thresholding plays a significant role in the arena of image segmentation. The main issue of multilevel image thresholding is to select the optimal combination of threshold value at different level. However, this problem has become challenging with the higher number of levels, because computational complexity is increased exponentially as the increase of number of threshold. To address this problem, this paper has proposed elephant herding optimization (EHO) based multilevel image thresholding technique for image segmentation. The EHO method has been inspired by the herding behaviour of elephant group in nature. Two well-known objective functions such as ‘Kapur's entropy' and ‘between-class variance method' have been used to determine the optimized threshold values for segmentation of different objects from an image. The performance of the proposed algorithm has been verified using a set of different test images taken from a well-known benchmark dataset named Berkeley Segmentation Dataset (BSDS). For comparative analysis, the results have been compared with three popular algorithms, e.g. cuckoo search (CS), artificial bee colony (ABC) and particle swarm optimization (PSO). It has been observed that the performance of the proposed EHO based image segmentation technique is efficient and promising with respect to the others in terms of the values of optimized thresholds, objective functions, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The algorithm also shows better convergence profile than the other methods discussed.

scholarly journals Enhanced Slime Mould Algorithm for Multilevel Thresholding Image Segmentation Using Entropy Measures

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1700
Author(s):  
Shanying Lin ◽  
Heming Jia ◽  
Laith Abualigah ◽  
Maryam Altalhi
Keyword(s):  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Similarity Index ◽  
Multilevel Thresholding ◽  
Slime Mould ◽  
Opposition Based Learning ◽  
Wilcoxon Rank Sum Test ◽  
Entropy Measures ◽  
Feature Similarity Index

Image segmentation is a fundamental but essential step in image processing because it dramatically influences posterior image analysis. Multilevel thresholding image segmentation is one of the most popular image segmentation techniques, and many researchers have used meta-heuristic optimization algorithms (MAs) to determine the threshold values. However, MAs have some defects; for example, they are prone to stagnate in local optimal and slow convergence speed. This paper proposes an enhanced slime mould algorithm for global optimization and multilevel thresholding image segmentation, namely ESMA. First, the Levy flight method is used to improve the exploration ability of SMA. Second, quasi opposition-based learning is introduced to enhance the exploitation ability and balance the exploration and exploitation. Then, the superiority of the proposed work ESMA is confirmed concerning the 23 benchmark functions. Afterward, the ESMA is applied in multilevel thresholding image segmentation using minimum cross-entropy as the fitness function. We select eight greyscale images as the benchmark images for testing and compare them with the other classical and state-of-the-art algorithms. Meanwhile, the experimental metrics include the average fitness (mean), standard deviation (Std), peak signal to noise ratio (PSNR), structure similarity index (SSIM), feature similarity index (FSIM), and Wilcoxon rank-sum test, which is utilized to evaluate the quality of segmentation. Experimental results demonstrated that ESMA is superior to other algorithms and can provide higher segmentation accuracy.

Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation

2017 ◽  
Vol 8 (4) ◽  
pp. 58-83 ◽  
Author(s):  
Abdul Kayom Md Khairuzzaman ◽  
Saurabh Chaudhury
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Multilevel Thresholding ◽  
Bacterial Foraging Optimization ◽  
Kapur’S Entropy ◽  
Moth Flame Optimization Algorithm ◽  
Popular Image ◽  
Better Than

Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is compared with PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based methods. The results are analyzed objectively using the fitness function and the Peak Signal to Noise Ratio (PSNR) values. It is found that MFO based multilevel thresholding method performs better than the PSO and BFO based methods.

Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation

2018 ◽  
pp. 771-797
Author(s):  
Abdul Kayom Md Khairuzzaman ◽  
Saurabh Chaudhury
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Multilevel Thresholding ◽  
Bacterial Foraging Optimization ◽  
Swarm Optimization ◽  
Kapur’S Entropy ◽  
Moth Flame Optimization Algorithm ◽  
Popular Image

Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is compared with PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based methods. The results are analyzed objectively using the fitness function and the Peak Signal to Noise Ratio (PSNR) values. It is found that MFO based multilevel thresholding method performs better than the PSO and BFO based methods.

Symbiotic Organisms Search Optimization for Multilevel Image Thresholding

2020 ◽  
Vol 11 (2) ◽  
pp. 31-61
Author(s):  
Falguni Chakraborty ◽  
Provas Kumar Roy ◽  
Debashis Nandi
Keyword(s):  
Image Segmentation ◽  
Heuristic Algorithms ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Symbiotic Relationship ◽  
Symbiotic Organisms Search ◽  
Image Thresholding ◽  
Prime Concern ◽  
Feature Similarity Index ◽  
Symbiotic Organisms

Determination of optimum thresholds is the prime concern of any multilevel image thresholding technique. The traditional methods for multilevel thresholding are computationally expensive, time-consuming, and also suffer from lack of accuracy and stability. To address this issue, the authors propose a new methodology for multilevel image thresholding based on a recently developed meta-heuristic algorithm, Symbiotic Organisms Search (SOS). The SOS algorithm has been inspired by the symbiotic relationship among the organism in nature. This article has utilized the concept of the symbiotic relationship among the organisms to optimize three objective functions: Otsu's between class variance and Kapur's and Tsallis entropy for image segmentation. The performance of the SOS based image segmentation algorithm has been evaluated using a set of benchmark images and has been compared with four recent meta-heuristic algorithms. The algorithms are compared in terms of effectiveness and consistency. The quality of the algorithms has been estimated by some well-defined quality metrics such as peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), and, feature similarity index (FSIM). The experimental results of the algorithms reveal that the balance of intensification and diversification of the SOS algorithm to achieve the global optima is better than others.

scholarly journals Comparative analysis between non-linear wavelet based image denoising techniques

2018 ◽  
Vol 5 ◽  
pp. 58-67
Author(s):  
Milan Chikanbanjar
Keyword(s):  
Visual Information ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Hard Thresholding ◽  
Structural Similarity Index ◽  
Thresholding Method ◽  
Non Linear ◽  
Good Quality Image

Digital images have been a major form of transmission of visual information, but due to the presence of noise, the image gets corrupted. Thus, processing of the received image needs to be done before being used in an application. Denoising of image involves data manipulation to remove noise in order to produce a good quality image retaining different details. Quantitative measures have been used to show the improvement in the quality of the restored image by the use of various thresholding techniques by the use of parameters mainly, MSE (Mean Square Error), PSNR (Peak-Signal-to-Noise-Ratio) and SSIM (Structural Similarity index). Here, non-linear wavelet transform denoising techniques of natural images are studied, analyzed and compared using thresholding techniques such as soft, hard, semi-soft, LevelShrink, SUREShrink, VisuShrink and BayesShrink. On most of the tests, PSNR and SSIM values for LevelShrink Hard thresholding method is higher as compared to other thresholding methods. For instance, from tests PSNR and SSIM values of lena image for VISUShrink Hard, VISUShrink Soft, VISUShrink Semi Soft, LevelShrink Hard, LevelShrink Soft, LevelShrink Semi Soft, SUREShrink, BayesShrink thresholding methods at the variance of 10 are 23.82, 16.51, 23.25, 24.48, 23.25, 20.67, 23.42, 23.14 and 0.28, 0.28, 0.28, 0.29, 0.22, 0.25, 0.16 respectively which shows that the PSNR and SSIM values for LevelShrink Hard thresholding method is higher as compared to other thresholding methods, and so on. Thus, it can be stated that the performance of LevelShrink Hard thresholding method is better on most of tests.

scholarly journals Improved ECG Watermarking Technique Using Curvelet Transform

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2941 ◽  
Author(s):  
Lalit Mohan Goyal ◽  
Mamta Mittal ◽  
Ranjeeta Kaushik ◽  
Amit Verma ◽  
Iqbaldeep Kaur ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Curvelet Transform ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Large Size ◽  
Residual Difference ◽  
Low Performance ◽  
Index Measure

Hiding data in electrocardiogram signals are a big challenge due to the embedded information that can hamper the accuracy of disease detection. On the other hand, hiding data into ECG signals provides more security for, and authenticity of, the patient’s data. Some recent studies used non-blind watermarking techniques to embed patient information and data of a patient into ECG signals. However, these techniques are not robust against attacks with noise and show a low performance in terms of parameters such as peak signal to noise ratio (PSNR), normalized correlation (NC), mean square error (MSE), percentage residual difference (PRD), bit error rate (BER), structure similarity index measure (SSIM). In this study, an improved blind ECG-watermarking technique is proposed to embed the information of the patient’s data into the ECG signals using curvelet transform. The Euclidean distance between every two curvelet coefficients was computed to cluster the curvelet coefficients and after this, data were embedded into the selected clusters. This was an improvement not only in terms of extracting a hidden message from the watermarked ECG signals, but also robust against image-processing attacks. Performance metrics of SSIM, NC, PSNR and BER were used to measure the superiority of presented work. KL divergence and PRD were also used to reveal data hiding in curvelet coefficients of ECG without disturbing the original signal. The simulation results also demonstrated that the clustering method in the curvelet domain provided the best performance—even when the hidden messages were large size.

scholarly journals An Efficient Parallel Block Compressive Sensing Scheme for Medical Signals and Image Compression

2021 ◽  
Author(s):  
Parnasree Chakraborty ◽  
Tharini C
Keyword(s):  
Compressive Sensing ◽  
Performance Metrics ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Rapid Development ◽  
Similarity Index ◽  
Structural Similarity ◽  
Compression Technique ◽  
Block Processing ◽  
Reconstruction Time

Abstract With rapid development of real-time and dynamic application, Compressive Sensing or Compressed sensing (CS) has been used for medical image and biomedical signal compression in the last decades. The performance of CS based compression is mostly dependent on decoding methods rather than the CS encoding methods used in practice. Many CS encoding and decoding algorithms have been reported in literature. However, the comparative study on performance metrics of CS encoding with block processing and without block processing is not investigated by the researchers so far. This paper proposes block CS based medical images and signals compression technique and the proposed technique is compared with standard CS compression. The proposed algorithm divides the input medical images and signals to blocks and each block is processed parallel to enable faster computation. Three performance indices, i.e., the peak signal to noise ratio (PSNR), reconstruction time (RT) and structural similarity index (SSIM) were tested to observe their changes with respect to compression ratio. The results showed that block CS algorithm had better performance than standard CS based compression. More specifically, the parallel block CS reported the best results than standard CS with respect to less reconstruction time and satisfactory PSNR and SSIM.

scholarly journals Weighted lp Norm Sparse Error Constraint Based ADMM for Image Denoising

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jiucheng Xu ◽  
Nan Wang ◽  
Zhanwei Xu ◽  
Keqiang Xu
Keyword(s):  
Image Denoising ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Lp Norm ◽  
Alternating Direction ◽  
Proposed Model ◽  
Feature Similarity Index ◽  
Constraint Problem ◽  
Error Constraint

In the process of image denoising, the accurate prior knowledge cannot be learned due to the influence of noise. Therefore, it is difficult to obtain better sparse coefficients. Based on this consideration, a weighted lp norm sparse error constraint (WPNSEC) model is proposed. Firstly, the suitable setting of power p in the lp norm is made a detailed analysis. Secondly, the proposed model is extended to color image denoising. Since the noise of RGB channels has different intensities, a weight matrix is introduced to measure the noise levels of different channels, and a multichannel weighted lp norm sparse error constraint algorithm is proposed. Thirdly, in order to ensure that the proposed algorithm is tractable, the multichannel WPNSEC model is converted into an equality constraint problem solved via alternating direction method of multipliers (ADMM) algorithm. Experimental results on gray image and color image datasets show that the proposed algorithms not only have higher peak signal-to-noise ratio (PSNR) and feature similarity index (FSIM) but also produce better visual quality than competing image denoising algorithms.

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