Chetia Rajib, Sahu Partha Pratim
Department of ECE, Tezpur University, Tezpur, Napam, Assam 784028 India.
Department of ECE, Central Institute of Technology (CIT), Kokrajhar, BTAD, Assam 783370 India.
Arab J Sci Eng. 2022 Jan 30:1-12. doi: 10.1007/s13369-021-06511-9.
The COVID-19 outbreak requires urgent public health attention throughout the world due to having its fast human to human transmission. As per the guidelines of the World Health Organization, rapid testing, vaccination, and isolation are the only options to break the chain of COVID-19 infection. Lung computed tomography (CT) plays a prime role in the accurate detection of COVID-19. For detection and pattern analysis of COVID-19, here an improved Sobel quantum edge extraction with non-maximum suppression and adaptive threshold (ISQEENSAT) has been employed to extract clinical information of infected lungs suppressing minimal noises present in the chest. In comparison with conventional classical edge extraction operators, the proposed technique can detect more sharp and accurate clinical edges of peripheral ground-glass opacity that appeared in the initial stage of COVID-19 patients. The edge extraction results assure the detection and differentiation of COVID-19 infection. ISQEENSAT can be a useful tool for assisting COVID-19 analysis and can help the physician to detect the region how much it has infected.
The online version contains supplementary material available at 10.1007/s13369-021-06511-9.
由于新冠病毒具有快速的人际传播能力,其爆发在全球范围内需要紧急的公共卫生关注。根据世界卫生组织的指导方针,快速检测、疫苗接种和隔离是打破新冠病毒感染链的唯一选择。肺部计算机断层扫描(CT)在新冠病毒的准确检测中起着主要作用。为了对新冠病毒进行检测和模式分析,本文采用了一种改进的带有非极大值抑制和自适应阈值的索贝尔量子边缘提取方法(ISQEENSAT)来提取受感染肺部的临床信息,同时抑制胸部存在的最小噪声。与传统的经典边缘提取算子相比,该技术能够检测出新冠病毒患者初期出现的外周磨玻璃影更清晰、准确的临床边缘。边缘提取结果确保了对新冠病毒感染的检测和区分。ISQEENSAT可以成为辅助新冠病毒分析的有用工具,并帮助医生检测感染区域的感染程度。
在线版本包含可在10.1007/s13369-021-06511-9获取的补充材料。