This paper presents an improved discrete quantum particle swarm optimization (IDQPSO) for 2-D maximum entropic multi-threshold imagesegmentation algorithm. Firstly, particle swarm binary-encoded method based on 2-D t...
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(纸本)9781467371896
This paper presents an improved discrete quantum particle swarm optimization (IDQPSO) for 2-D maximum entropic multi-threshold imagesegmentation algorithm. Firstly, particle swarm binary-encoded method based on 2-D threshold is proposed. Additionally, new particle evolution strategy is proposed to avoid converging on local optimum and accelerate searching progress. Additionally, experiments are conducted by comparing IDQPSO with other state-of-the-art methods such as QGA, NBPSO and BQPSO. The results show that IDPQSO outperforms other algorithms at precision, efficiency and stability.
To improve the diagnosis of Lupus Nephritis (LN), a multilevel LN imagesegmentation method is developed in this paper based on an improved slime mould algorithm. The search of the optimal threshold set is key to mult...
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To improve the diagnosis of Lupus Nephritis (LN), a multilevel LN imagesegmentation method is developed in this paper based on an improved slime mould algorithm. The search of the optimal threshold set is key to multilevel thresholding image segmentation (MLTIS). It is well known that swarm-based methods are more efficient than the traditional methods because of the high complexity in finding the optimal threshold, especially when performing image partitioning at high threshold levels. However, swarm-based methods tend to obtain the poor quality of the found segmentation thresholds and fall into local optima during the process of segmentation. Therefore, this paper proposes an ASMA-based MLTIS approach by combining an improved slime mould algorithm (ASMA), where ASMA is mainly implemented by introducing the position update mechanism of the artificial bee colony (ABC) into the SMA. To prove the superiority of the ASMA-based MLTIS method, we first conducted a comparison experiment between ASMA and 11 peers using 30 test functions. The experimental results fully demonstrate that ASMA can obtain high-quality solutions and almost does not suffer from premature convergence. Moreover, using standard images and LN images, we compared the ASMA-based MLTIS method with other peers and evaluated the segmentation results using three evaluation indicators called PSNR, SSIM, and FSIM. The proposed ASMA can be an excellent swarm intelligence optimization method that can maintain a delicate balance during the segmentation process of LN images, and thus the ASMA-based MLTIS method has great potential to be used as an imagesegmentation method for LN images. The lastest updates for the SMA algorithm are available in https://***/***.
Medical imagesegmentation is crucial in using digital images for disease diagnosis, particularly in post-processing tasks such as analysis and disease identification. segmentation of magnetic resonance imaging (MRI) ...
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Medical imagesegmentation is crucial in using digital images for disease diagnosis, particularly in post-processing tasks such as analysis and disease identification. segmentation of magnetic resonance imaging (MRI) and computed tomography images pose distinctive challenges attributed to factors such as inadequate illumination during the image acquisition process. multilevelthresholding is a widely adopted method for imagesegmentation due to its effectiveness and ease of implementation. However, the primary challenge lies in selecting the optimal set of thresholds to achieve accurate segmentation. While Otsu's between-class variance and Kapur's entropy assist in identifying optimal thresholds, their application to cases requiring more than two thresholds can be computationally intensive. Meta-heuristic algorithms are commonly employed in literature to calculate the threshold values;however, they have limitations such as a lack of precise convergence and a tendency to become stuck in local optimum solutions. In this paper, we introduce an improved chameleon swarm algorithm (ICSA) to address these limitations. ICSA is designed for imagesegmentation and global optimization tasks, aiming to improve the precision and efficiency of threshold selection in medical imagesegmentation. ICSA introduces the concept of the "best random mutation strategy" to enhance the search capabilities of the standard chameleon swarm algorithm (CSA). This strategy leverages three distribution functions-Levy, Gaussian, and Cauchy-for mutating search individuals. These diverse distributions contribute to improved solution quality and help prevent premature convergence. We conduct comprehensive experiments using the IEEE CEC'20 complex optimization benchmark test suite to evaluate ICSA's performance. Additionally, we employ ICSA in imagesegmentation, utilizing Otsu's approach and Kapur's entropy as fitness functions to determine optimal threshold values for a set of MRI images. Comparative
Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people's safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tom...
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Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people's safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO's initialization step. MRFO-OBL algorithm can solve the imagesegmentation problem using multilevelthresholding. The proposed MRFO-OBL is evaluated using Otsu's method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu's method for all the used metrics.
Background Moth-flame optimization will meet the premature and stagnation phenomenon when encountering difficult optimization tasks. Objective This paper presented a quasi-reflection moth-flame optimization algorithm ...
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Background Moth-flame optimization will meet the premature and stagnation phenomenon when encountering difficult optimization tasks. Objective This paper presented a quasi-reflection moth-flame optimization algorithm with refraction learning called QRMFO to strengthen the property of ordinary MFO and apply it in various application fields to overcome shortcomings. Methods In the proposed QRMFO, quasi-reflection-based learning increases the diversity of the population and expands the search space on the iteration jump phase;refraction learning improves the accuracy of the potential optimal solution. Results Several experiments are conducted to evaluate the superiority of the proposed QRMFO in the paper;first of all, the CEC2017 benchmark suite is utilized to estimate the capability of QRMFO when dealing with the standard test sets compared with the state-of-the-art algorithms;afterward, QRMFO is adopted to deal with multilevel thresholding image segmentation problems and real medical diagnosis case. Conclusion Simulation results and discussions show that the proposed optimizer is superior to the basic MFO and other advanced methods in terms of convergence rate and solution accuracy.
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