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| 混沌映射麻雀搜索优化OTSU的图像分割算法 |
| Chaotic mapping sparrow search optimized OTSU for image segmentation algorithm |
| 投稿时间：2024-03-25 |
| DOI：10.3969/j.issn.1005-5630.202403250061 |
| 中文关键词: 图像分割 麻雀搜索算法 OTSU算法 PWLCM混沌映射 皮肤镜图像 |
| 英文关键词:image segmentation sparrow search algorithm OTSU algorithm PWLCM chaotic mapping dermatoscope image |
| 基金项目: |
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| 摘要点击次数: 934 |
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| 中文摘要: |
| 针对皮肤镜图像病灶分割存在耗时长且过于主观等问题，提出一个改进的麻雀优化算法(improve sparrow search algorithm, ISSA)来优化OTSU阈值分割。算法通过模拟麻雀觅食和反捕食行为的麻雀搜索算法，将图像的类间方差作为适应度函数，在种群初始化引入分段线性混沌映射(piecewise linear chaotic map，PWLCM)，提高了算法的搜索空间和寻优性能，帮助算法及时跳出局部最优。将本文提出的算法与常用的粒子群优化算法(particle swarm optimizer, PSO)、灰熊优化算法(grey wolf optimizer, GWO)和麻雀搜索算法(sparrow search algorithm, SSA)进行对比，采用皮肤镜图像进行双阈值OTSU分割实验，结果表明，所提出的ISSA不仅在寻优方面有所增强，迭代的次数相比于PSO、GWO和SSA算法也分别减少了92.2%、68.2%和41.7%，运行时间减少了66.4%、43.4%和21.1%，证明了该算法的可行性。 |
| 英文摘要: |
| To address the time-consuming and overly subjective challenges of lesion segmentation in dermatoscope images, an improved sparrow optimization algorithm (ISSA) is proposed to optimize the OTSU threshold segmentation. The algorithm, inspired by the foraging and anti-predation behavior of sparrows, utilizes the inter-class variance as the fitness function. By incorporating the piecewise linear chaotic map (PWLCM) in population initialization, it enhances the algorithm's search space and optimization performance, facilitating the escape from local optima in a timely manner. The proposed ISSA is compared with commonly used optimization algorithms, including the particle swarm optimizer (PSO), grey wolf optimizer (GWO), and the original sparrow search algorithm (SSA), in dual-threshold OTSU segmentation experiments on dermatoscope images. The results demonstrate that ISSA not only shows an improvement in optimization but also reduces the number of iterations by 92.2%, 68.2%, and 41.7% and the runtime by 66.4%, 43.4%, and 21.1% compared to PSO, GWO, and SSA, respectively. |
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