Electromagnetic Optimization Algorithm Research Articles

Efficient Approach to Color Image Segmentation Based on Multilevel Thresholding Using EMO Algorithm by Considering Spatial Contextual Information.

The process of image segmentation is partitioning an image into its constituent parts and is a significant approach for extracting interesting features from images. Over a couple of decades, many efficient image segmentation approaches have been formulated for various applications. Still, it is a challenging and complex issue, especially for color image segmentation. To moderate this difficulty, a novel multilevel thresholding approach is proposed in this paper based on the electromagnetism optimization (EMO) technique with an energy curve, named multilevel thresholding based on EMO and energy curve (MTEMOE). To compute the optimized threshold values, Otsu's variance and Kapur's entropy are deployed as fitness functions; both values should be maximized to locate optimal threshold values. In both Kapur's and Otsu's methods, the pixels of an image are classified into different classes based on the threshold level selected on the histogram. Optimal threshold levels give higher efficiency of segmentation; the EMO technique is used to find optimal thresholds in this research. The methods based on an image's histograms do not possess the spatial contextual information for finding the optimal threshold levels. To abolish this deficiency an energy curve is used instead of the histogram and this curve can establish the spatial relationship of pixels with their neighbor pixels. To study the experimental results of the proposed scheme, several color benchmark images are considered at various threshold levels and compared with other meta-heuristic algorithms: multi-verse optimization, whale optimization algorithm, and so on. The investigational results are illustrated in terms of mean square error, peak signal-to-noise ratio, the mean value of fitness reach, feature similarity, structural similarity, variation of information, and probability rand index. The results reveal that the proposed MTEMOE approach overtops other state-of-the-art algorithms to solve engineering problems in various fields.

Efficient Image Segmentation of Natural Images with Noise using Energy Curved Based on Electromagnetism Optimization Algorithm

The method of digital image processing is an important step for extraction features from images, especially details from the medical image by dividing the image into various regions. In the last two decades, many significant image segmentation schemes modeled for different applications like medical to weather forecasting by images from the satellite. From available methods, multilevel thresholding of pixel values using the histogram of an image is an exceedingly important method developed for vide range of applications. Otsu’s method is the primer multilevel thresholding method based on the maximizing the inter-class variance based on the histogram. In Otsu’s method, the pixels of an image are grouped into different regions based on threshold levels calculated on the histogram of the image. All the pixels are segmented into two groups based on threshold level in the case of bi-level thresholding. The effectiveness of segmentation is depends on threshold levels, efficiency can be enhanced by computing optimized threshold level, Electromagnetism Optimization (EMO) technique used for this purpose in this paper. The techniques based on histograms of image are not holding the spatial particulars of contextual information for computing the optimized threshold levels, to nullify this shortcoming Energy Curve is used as an alternative of histogram, which is restore the special relation of pixels with its neighbor pixels. To examine the results several benchmark images considered, the final results shows that the method with Otsu’s technique with EMO on energy curve is giving better results as compared with methods based on the image histogram.

Automatic segmentation system for liver tumors based on the multilevel thresholding and electromagnetism optimization algorithm

AbstractIn this article, we propose an automated segmentation system for liver tumors using magnetic resonance imaging and computed tomography. The proposed system is based on the algorithm of multilevel thresholding with electromagnetism optimization (EMO). The system starts with visualizing a patient's digital communication in medicine (DICOM) abdominal data set in three views. Two‐stage active contour segmentation methods that integrate region‐based local and global techniques using the active geodesic contour technique are proposed to segment the liver. To increase the accuracy and speed of segmentation for liver images, we identify the optimal threshold of the image segmentation method based on EMO with Otsu and Kapur algorithms. EMO offers interesting search capabilities while keeping a low computational cost. The proposed system was tested using a set of five DICOM data sets. All images were of the same size and stored in JPEG format (512 × 512 pixels). Experimental results illustrate that the proposed system outperforms state‐of‐the‐art methods such as the watershed algorithm. The average sensitivity, specificity, and accuracy of the segmented liver using the active contour model were 97.05%, 99.88%, and 98.47%, respectively. Moreover, the average sensitivity, specificity, and accuracy of the segmented liver tumor results were 94.15%, 99.57%, and 96.86%, respectively.

A novel pectoral muscle segmentation from scanned mammograms using EMO algorithm.

Mammogram images are majorly used for detecting the breast cancer. The level of positivity of breast cancer is detected after excluding the pectoral muscle from mammogram images. Hence, it is very significant to identify and segment the pectoral muscle from the mammographic images. In this work, a new multilevel thresholding, on the basis of electro-magnetism optimization (EMO) technique, is proposed. The EMO works on the principle of attractive and repulsive forces among the charges to develop the members of a population. Here, both Kapur's and Otsu based cost functions are employed with EMO separately. These standard functions are executed over the EMO operator till the best solution is achieved. Thus, optimal threshold levels can be identified for the considered mammographic image. The proposed methodology is applied on all the three twenty-two mammogram images available in mammographic image analysis society dataset, and successful segmentation of the pectoral muscle is achieved for majority of the mammogram images. Hence, the proposed algorithm is found to be robust for variations in the pectoral muscle.

Reduction of PAPR in OFDM Signals by using Electromagnetism Optimization Algorithm

Orthogonal Frequency Division Multiplexing grown to be a trendy modulation process inside impressive tempo wirelesscommunications. It is more advantageous over remaining technologies, even though it has some problems also. The high PAPR (Peak toAverage Power Ratio) is the main difficulty which causes non linearity at the receiving end. To reduce the PAPR so many techniques areproposed in previous such as clipping and filtering, SLM, CE, SA, PTS and so on. PTS (Partial Transmit Sequence) is highly successive thananother. In this paper PTS technique is used to reduce the PAPR but there is one problem with PTS technique, it introduces a high amount of complexity. So that to reduce such complexity and get better PAPR we planned to implement a novel based stochastic optimization algorithm which is EM (Electromagnetism-like) algorithm. By using EM get the better PAPR than PTS

Circle detection using electro-magnetism optimization

Nature-inspired computing has yielded remarkable applications of collective intelligence which considers simple elements for solving complex tasks by common interaction. On the other hand, automatic circle detection in digital images has been considered an important and complex task for the computer vision community that has devoted a tremendous amount of research, seeking for an optimal circle detector. This paper presents an algorithm for the automatic detection of circular shapes embedded into cluttered and noisy images without considering conventional Hough transform techniques. The approach is based on a nature-inspired technique known as the Electro-magnetism Optimization (EMO). It follows the electro-magnetism principle regarding a collective attraction–repulsion mechanism which manages particles towards an optimal solution. Each particle represents a solution by holding a charge which is related to the objective function to be optimized. The algorithm uses the encoding of three non-collinear points embedded into an edge-only image as candidate circles. Guided by the values of the objective function, the set of encoded candidate circles (charged particles) are evolved using an EMO algorithm so that they can fit into actual circular shapes over the edge-only map of the image. Experimental evidence from several tests on synthetic and natural images which provide a varying range of complexity validates the efficiency of our approach regarding accuracy, speed and robustness.