Segmentation is a crucial phase in image processing because it simplifies the representation of an image and facilitates its analysis. The multilevel thresholding method is more efficient for segmenting digital mammograms compared to the classic bi-level thresholding since it uses a higher number of intensities to represent different regions in the image. In the literature, there are different techniques for multilevel segmentation; however, most of these approaches do not obtain good segmented images. In addition, they are computationally expensive. Recently, statistical criteria such as Otsu, Kapur, and cross-entropy have been utilized in combination with evolutionary and swarm-based strategies to investigate the optimal threshold values for multilevel segmentation. In this paper, an efficient methodology for multilevel segmentation is proposed using the Harris Hawks Optimization (HHO) algorithm and the minimum cross-entropy as a fitness function. To substantiate the results and effectiveness of the HHO-based method, it has been tested over a benchmark set of reference images, with the Berkeley segmentation database, and with medical images of digital mammography. The proposed HHO-based solver is verified based on other comparable optimizers and two machine learning algorithms K-means and the Fuzzy IterAg. The comparisons were performed based on three groups. This first one is to provide evidence of the optimization capabilities of the HHO using the Wilcoxon test, and the second is to verify segmented image quality using the PSNR, SSIM, and FSIM metrics. Then, the third way is to verify the segmented image comparing it with the ground-truth through the metrics PRI, GCE, and VoI. The experimental results, which are validated by statistical analysis, show that the introduced method produces efficient and reliable results in terms of quality, consistency, and accuracy in comparison with the other methods. This HHO-based method presents an improvement over other segmentation approaches that are currently used in the literature.
The population-based HHO was the most successful and popular optimization method currently. The HHO focuses on performance and provides a variety of search patterns based on random switching statements. It is a gradient-free optimization algorithm with several energetic and time-varying stages of exploration and exploitation tendencies. In spite of previous methods published in lower impact journals, the HHO was published in the Journal of Future Generation Computer Systems (FGCS) with an impact factor of 6.125 in 2019, and from the first day of publication, it has gotten growing consideration among researchers owing to its flexible structure, high performance, and first-rate results. The leading logic of the HHO technique is created according to some successful life patterns of Harris' hawks in nature called "surprise pounce". Due to the HHO technique's efficacy, there are many variants of HHO now in the best leading Elsevier and IEEE transaction journals.
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The HHO algorithm is a high performance and easy to code, and straightforward to understand optimizer, while it has some time-varying components. The primary method was published in a top prestigious computer science journal. In 2020, it turned into the most used method for solving any problem. This method's source codes are widely available in almost all programming languages, and it has both a latex template and word office file for the pleasure of users. This method is backed up with a 24-h online service for reacting to users' questions on the code..
How to cite?
Rodríguez-Esparza, Erick, Laura A. Zanella-Calzada, Diego Oliva, Ali Asghar Heidari, Daniel Zaldivar, Marco Pérez-Cisneros, and Loke Kok Foong. "An Efficient Harris Hawks-inspired Image Segmentation Method." Expert Systems with Applications (2020): 113428. https://doi.org/10.1016/j.eswa.2020.113428
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