Multilevel image thresholding with multimodal optimization

Taymaz Rahkar Farshi | Recep Demirci

Makale | 2021 | SPRINGER

Thresholding method is one of the most popular approaches for image segmentation where an objective function is defined in terms of threshold numbers and their locations in a histogram. If only a single threshold is considered, a segmented image with two classes is achieved. On the other hand, multiple classes in the output image are created with multilevel thresholding. Otsu and Kapur's procedures have been conventional steps for defining objective functions. Nevertheless, the fundamental problem with thresholding techniques is the determination of threshold numbers, which must be selected by the user. In that respect, thresholding . . . methods with both techniques are user-dependent, and may not be practical for real-time image processing applications. In this study, a novel thresholding algorithm without any objective function has been proposed. Histogram curve was considered as an objective function. The peaks and valley in histogram have been detected by means of multimodal particle swarm optimization algorithms. Accordingly, valleys between two peaks have been assigned as thresholds. Consequently, the developed scheme does not need any user intervention and finds the number of thresholds automatically. Furthermore, computation time is independent of the number of thresholds, whereas computation time in Otsu and Kapur procedures depends on the number of thresholds Daha fazlası Daha az

Vector quantization using whale optimization algorithm for digital image compression

Javad Rahebi

Makale | 2022 | SPRINGER

Today, much of the information is storing in images. To transfer information in the form of images, image compression is required. Compressing images reduces the size of images and sends them faster over the network. One of the most methods of image compression is the vector quantization. For vector quantization compression, the codebook is using in cryptography and decryption. The vector quantization compression method typically uses codebooks that are not optimized, which reduces the compression quality of the images. Choosing the optimal codebook makes compression of images with higher quality. Choosing the optimal codebook is a . . .difficult optimization problem and therefore requires intelligent algorithms to solve it. In this paper, the whale optimization algorithm is used to find the optimal codebook in image compression. Whale Optimization Algorithm has different search strategies and is an ideal algorithm for finding the optimal codebook in images compression. Implementation of the proposed algorithm for compression on several standard images shows that the proposed method compresses images with appropriate quality. The proposed method performs more efficient compression than the proposed algorithms such as particle swarm optimization, bat, and firefly algorithms. The signal-to-noise ratio of the proposed method is higher than the compared methods. Experiments on a set of standard images show the proposed method compared to the Fire Fly, Bat, and Differential evolution, Improved Particle Swarm Optimization, and Improved Differential Evolution methods with a compression execution time of 60.48 and 10.21, respectively. , 4.79, 5.09 and 3.94 decreased. The proposed method in compression has a higher PSNR index of about 17 than the Linde-Buzo-Gray method Daha fazlası Daha az

Patient privacy in smart cities by blockchain technology and feature selection with Harris Hawks Optimization (HHO) algorithm and machine learning

Haedar Al-Safi | Jorge Munilla | Javad Rahebi

Makale | 2022 | SPRINGER

A medical center in the smart cities of the future needs data security and confidentiality to treat patients accurately. One mechanism for sending medical data is to send information to other medical centers without preserving confidentiality. This method is not impressive because in treating people, the privacy of medical information is a principle. In the proposed framework, the opinion of experts from other medical centers for the treatment of patients is received and consider the best therapy. The proposed method has two layers. In the first layer, data transmission uses blockchain. In the second layer, blocks related to patient . . .s' records analyze by machine learning methods. Patient records place in a block of the blockchain. Block of patient sends to other medical centers. Each treatment center can recommend the proposed type of treatment and blockchain attachment and send it to all nodes and treatment centers. Each medical center receiving data of the patients, then predicts the treatment using data mining methods. Sending medical data between medical centers with blockchain and maintaining confidentiality is one of the innovations of this article. The proposed method is a binary version of the HHO algorithm for feature selection. Another innovation of this research is the use of majority voting learning in diagnosing the type of disease in medical centers. Implementation of the proposed system shows that the blockchain preserves data confidentiality of about 100. The reliability and reliability of the proposed framework are much higher than the centralized method. The result shows that the accuracy, sensitivity, and precision of the proposed method for diagnosing heart disease are 92.75, 92.15, and 95.69, respectively. The proposed method has a lower error in diagnosing heart disease from ANN, SVM, DT, RF, AdaBoost, and BN Daha fazlası Daha az

A hybrid firefly and particle swarm optimization algorithm applied to multilevel image thresholding

Taymaz Rahkar Farshi | Ahad K. Ardabili

Makale | 2021 | SPRINGER

There are many techniques for conducting image analysis and pattern recognition. This papers explores a way to optimize one of these techniques-image segmentation-with the help of a novel hybrid optimization algorithm. Image segmentation is mostly used for a semantic segmentation of images, and thresholding is one the most common techniques for performing this segmentation. Otsu's and Kapur's thresholding methods are two well-known approaches, both of which maximize the between-class variance and the entropy measure, respectively, in a gray image histogram. Both techniques were developed for bi-level thresholding. However, these tec . . .hniques can be extended to multilevel image thresholding. For this to occur, a large number of iterations are required to account for exact threshold values. However, various optimization techniques have been used to overcome this drawback. In this study, a hybrid firefly and particle swarm optimization algorithm has been applied to yield optimum threshold values in multilevel image thresholding. The proposed method has been assessed by comparing it with four well-known optimization algorithms. The comprehensive experiments reveal that the proposed method achieves better results in term of fitness value, PSNR, SSIM, FSIM, and SD Daha fazlası Daha az

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