多级结肠镜 组织病理 图像细分 使用粒子群优化技术
Anusree Kanadath1, J Angel Arul Jothi1, Siddhaling Urolagin1
1Department of Computer Science, Birla Institute of Science and Technology Pilani, Dubai Campus, Dubai International Academic City, Dubai, 345055 United Arab Emirates.
SN computer science
|June 12, 2023
概括
分数顺序达尔文粒子群集优化 (FODPSO) 有效地细分结肠镜 组织病理学图像用于病变检测. 与其他优化技术相比,这种先进的方法在识别癌症区域方面取得了更高的准确性.
科学领域:
- 医疗图像处理 医学图像处理
- 计算病理学计算病理学
- 生物医学工程 生物医学工程
背景情况:
- 组织病理学图像细分对于从组织样本诊断疾病至关重要.
- 在结肠镜图像中精确细分病变区域存在重大挑战.
- 现有的细分方法往往需要复杂的参数调整,可能缺乏稳定性.
研究的目的:
- 开发和评估一项先进的图像分段技术,用于结肠镜造型学图像.
- 为了准确地细分和识别结肠组织数据集中的病变区域.
- 将新型优化算法的性能与已建立的方法进行比较.
主要方法:
- 图像预处理被应用到结肠镜异位病理学图像.
- 多级图像值,作为一个优化问题,被用来进行细分.
- 分数顺序的达尔文粒子群优化 (FODPSO) 被用来确定最佳值,以及粒子群优化 (PSO) 和达尔文粒子群优化 (DPSO).
主要成果:
- 使用Otsu的判别标准的FODPSO算法在结肠镜数据集上实现了高分段精度 (0.89),子系数 (0.68) 和雅卡德指数 (0.52).
- 实施了后处理步骤,以完善细分的损伤区域.
- 与人工蜂群 (ABC) 和火算法相比,FODPSO表现出优越的性能.
结论:
- 分数顺序达尔文粒子群集优化 (FODPSO) 是一种高效的方法,用于细分损伤区域在结肠镜异位病理学图像.
- 拟议的方法为胃肠道病理学中的计算机辅助诊断提供了更高的准确性和可靠性.
- 对于癌症检测,FODPSO在自动化医学图像分析方面提出了一个有前途的进步.
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