Segmentation of brain MRI using an altruistic Harris Hawks’ Optimization algorithm; Knowledge-Based Systems; Vol. 232
| Parent link: | Knowledge-Based Systems Vol. 232.— 2021.— [107468, 19 p.] |
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| مؤلف مشترك: | |
| مؤلفون آخرون: | , , , |
| الملخص: | Title screen Segmentation is an essential requirement in medicine when digital images are used in illness diagnosis, especially, in posterior tasks as analysis and disease identification. An efficient segmentation of brain Magnetic Resonance Images (MRIs) is of prime concern to radiologists due to their poor illumination and other conditions related to de acquisition of the images. Thresholding is a popular method for segmentation that uses the histogram of an image to label different homogeneous groups of pixels into different classes. However, the computational cost increases exponentially according to the number of thresholds. In this paper, we perform the multi-level thresholding using an evolutionary metaheuristic. It is an improved version of the Harris Hawks Optimization (HHO) algorithm that combines the chaotic initialization and the concept of altruism. Further, for fitness assignment, we use a hybrid objective function where along with the cross-entropy minimization, we apply a new entropy function, and leverage weights to the two objective functions to form a new hybrid approach. The HHO was originally designed to solve numerical optimization problems. Earlier, the statistical results and comparisons have demonstrated that the HHO provides very promising results compared with well-established metaheuristic techniques. In this article, altruism has been incorporated into the HHO algorithm to enhance its exploitation capabilities. We evaluate the proposed method over 10 benchmark images from the WBA database of the Harvard Medical School and 8 benchmark images from the Brainweb dataset using some standard evaluation metrics. On the Harvard WBA dataset, a Peak Signal to Noise Ratio (PSNR) of 26.61 and a Structural Similarity Index (SSIM) of 0.92 are achieved using 5 thresholds. For the same scenario, using the Brainweb dataset, a PSNR of 24.77 and SSIM of 0.86 are obtained. The obtained results justify the superiority of the proposed approach compared to existing state-of-the-art methods and baseline methods. Режим доступа: по договору с организацией-держателем ресурса |
| اللغة: | الإنجليزية |
| منشور في: |
2021
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://doi.org/10.1016/j.knosys.2021.107468 |
| التنسيق: | الكتروني فصل الكتاب |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=666007 |
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| 200 | 1 | |a Segmentation of brain MRI using an altruistic Harris Hawks’ Optimization algorithm |f R. Bandyopadhyay, R. Kundu, D. A. Oliva Navarro, R. Sarkar | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 64 tit.] | ||
| 330 | |a Segmentation is an essential requirement in medicine when digital images are used in illness diagnosis, especially, in posterior tasks as analysis and disease identification. An efficient segmentation of brain Magnetic Resonance Images (MRIs) is of prime concern to radiologists due to their poor illumination and other conditions related to de acquisition of the images. Thresholding is a popular method for segmentation that uses the histogram of an image to label different homogeneous groups of pixels into different classes. However, the computational cost increases exponentially according to the number of thresholds. In this paper, we perform the multi-level thresholding using an evolutionary metaheuristic. It is an improved version of the Harris Hawks Optimization (HHO) algorithm that combines the chaotic initialization and the concept of altruism. Further, for fitness assignment, we use a hybrid objective function where along with the cross-entropy minimization, we apply a new entropy function, and leverage weights to the two objective functions to form a new hybrid approach. The HHO was originally designed to solve numerical optimization problems. Earlier, the statistical results and comparisons have demonstrated that the HHO provides very promising results compared with well-established metaheuristic techniques. In this article, altruism has been incorporated into the HHO algorithm to enhance its exploitation capabilities. We evaluate the proposed method over 10 benchmark images from the WBA database of the Harvard Medical School and 8 benchmark images from the Brainweb dataset using some standard evaluation metrics. On the Harvard WBA dataset, a Peak Signal to Noise Ratio (PSNR) of 26.61 and a Structural Similarity Index (SSIM) of 0.92 are achieved using 5 thresholds. For the same scenario, using the Brainweb dataset, a PSNR of 24.77 and SSIM of 0.86 are obtained. The obtained results justify the superiority of the proposed approach compared to existing state-of-the-art methods and baseline methods. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | |t Knowledge-Based Systems | ||
| 463 | |t Vol. 232 |v [107468, 19 p.] |d 2021 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a magnetic Resonance Imaging (MRI) | |
| 610 | 1 | |a brain MRI segmentation | |
| 610 | 1 | |a metaheuristic | |
| 610 | 1 | |a altruism | |
| 610 | 1 | |a hybrid objective function | |
| 610 | 1 | |a магнитно-резонансная томография | |
| 610 | 1 | |a сегментация | |
| 610 | 1 | |a головной мозг | |
| 701 | 1 | |a Bandyopadhyay |b R. |g Rajarshi | |
| 701 | 1 | |a Kundu |b R. |g Rohit | |
| 701 | 1 | |a Oliva Navarro |b D. A. |c specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University |f 1983- |g Diego Alberto |3 (RuTPU)RU\TPU\pers\37366 | |
| 701 | 1 | |a Sarkar |b R. |g Ram | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа информационных технологий и робототехники |b Отделение информационных технологий |3 (RuTPU)RU\TPU\col\23515 |
| 801 | 2 | |a RU |b 63413507 |c 20211129 |g RCR | |
| 856 | 4 | |u https://doi.org/10.1016/j.knosys.2021.107468 | |
| 942 | |c CF | ||