论文标题
使用香农熵和Markov-random-Field检测肺CT扫描图像中的COVID-19病变的萤火虫 - 算法支持的方案
Firefly-Algorithm Supported Scheme to Detect COVID-19 Lesion in Lung CT Scan Images using Shannon Entropy and Markov-Random-Field
论文作者
论文摘要
由冠状病毒疾病引起的肺炎（Covid-19）是全球主要威胁之一，研究人员提出了许多检测和治疗程序，即COVID-19。拟议的工作旨在提出一种自动图像处理方案，以从患者记录的肺CT扫描图像（CTI）中提取COVID-19。该计划实施以下程序； （i）图像预处理以增强共同的19个病变，（ii）提取病变的图像后处理，以及（iii）（iii）在提取的病变段和地面真实图像（GTI）之间执行相对分析。这项工作实现了基于萤火虫算法和香农熵（FA+SE）的多阈值，以增强肺炎病变并实现马尔可夫兰助焊剂（MRF）分割，以更好的准确性提取病变。使用从现有图像数据集获得的一类COVID-19 CTI进行测试和验证该提出的方案，并评估了实验结果以证明该方案的临床意义。拟议的工作有助于达到共vid-19病变分割期间的平均准确性> 92％，并且将来可以使用它来检查Covid-19患者的实际临床肺CTI。
The pneumonia caused by Coronavirus disease (COVID-19) is one of major global threat and a number of detection and treatment procedures are suggested by the researchers for COVID-19. The proposed work aims to suggest an automated image processing scheme to extract the COVID-19 lesion from the lung CT scan images (CTI) recorded from the patients. This scheme implements the following procedures; (i) Image pre-processing to enhance the COVID-19 lesions, (ii) Image post-processing to extract the lesions, and (iii) Execution of a relative analysis between the extracted lesion segment and the Ground-Truth-Image (GTI). This work implements Firefly Algorithm and Shannon Entropy (FA+SE) based multi-threshold to enhance the pneumonia lesion and implements Markov-Random-Field (MRF) segmentation to extract the lesions with better accuracy. The proposed scheme is tested and validated using a class of COVID-19 CTI obtained from the existing image datasets and the experimental outcome is appraised to authenticate the clinical significance of the proposed scheme. The proposed work helped to attain a mean accuracy of >92% during COVID-19 lesion segmentation and in future, it can be used to examine the real clinical lung CTI of COVID-19 patients.
