Faragallah Osama S, El-Hoseny Heba M, El-Sayed Hala S
Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944 Saudi Arabia.
Department of Computer Science, The Higher Future Institute for Specialized Technological Studies, El Shorouk, Egypt.
J Ambient Intell Humaniz Comput. 2023;14(7):9217-9232. doi: 10.1007/s12652-022-04425-4. Epub 2022 Oct 21.
In computer vision segmentation field, super pixel identity has become an important index in the recently segmentation algorithms especially in medical images. Simple Linear Iterative Clustering (SLIC) algorithm is one of the most popular super pixel methods as it has a great robustness, less sensitive to the image type and benefit to the boundary recall in different kinds of image processing. Recently, COVID-19 severity increased with the lack of an effective treatment or vaccine. As the Corona virus spreads in an unknown manner, th-ere is a strong need for segmenting the lungs infected regions for fast tracking and early detection, no matter how small. This may consider difficult to be achieved with traditional segmentation techniques. From this perspective, this paper presents an efficient modified central force optimization (MCFO)-based SLIC segmentation algorithm to discuss chest CT images for detecting the positive COVID-19 cases. The proposed MCFO-based SLIC segmentation algorithm performance is evaluated and compared with the thresholding segmentation algorithm using different evaluation metrics such as accuracy, boundary recall, F-measure, similarity index, MCC, Dice, and Jaccard. The outcomes demonstrated that the proposed MCFO-based SLIC segmentation algorithm has achieved better detection for the small infected regions in CT lung scans than the thresholding segmentation.
在计算机视觉分割领域，超像素一致性已成为近期分割算法尤其是医学图像分割算法中的一项重要指标。简单线性迭代聚类（SLIC）算法是最受欢迎的超像素方法之一，因为它具有很强的鲁棒性，对图像类型不太敏感，并且有利于不同图像处理中的边界召回。近期，由于缺乏有效的治疗方法或疫苗，新冠疫情的严重程度不断增加。随着新冠病毒以未知方式传播，迫切需要对肺部感染区域进行分割，以便快速追踪和早期检测，无论感染区域多么小。这可能被认为难以用传统分割技术实现。从这个角度来看，本文提出了一种基于改进中心力优化（MCFO）的高效SLIC分割算法，用于分析胸部CT图像以检测新冠阳性病例。使用不同的评估指标（如准确率、边界召回率、F值、相似性指数、马修斯相关系数、骰子系数和杰卡德系数）对所提出的基于MCFO的SLIC分割算法性能进行评估，并与阈值分割算法进行比较。结果表明，所提出的基于MCFO的SLIC分割算法在CT肺部扫描中对小感染区域的检测效果优于阈值分割算法。
