S N Kumar, S Kannadhasan, Zafar Sherin, Kumar H Ajay
Department of EEE, Amal Jyothi College of Engineering, Kanjirappally, Kerala, India.
Department of ECE, Study World College of Engineering, Coimbatore, Tamilnadu, India.
Heliyon. 2024 Mar 16;10(6):e27798. doi: 10.1016/j.heliyon.2024.e27798. eCollection 2024 Mar 30.
Edge detection is a vital aspect of medical image processing, playing a key role in delineating borders and contours within images. This capability is instrumental for various applications, including segmentation, feature extraction, and diagnostic procedures in the realm of medical imaging. COVID-19 is a deadly disease affecting people in most of countries in the world. COVID-19 is due to the coronavirus which belongs to the family of RNA viruses and causes various symptoms such as pneumonia, fever, breathing difficulty, and lung infection. ROI extraction plays a vital role in disease diagnosis and therapeutic treatment. CT scans can help detect abnormalities in the lungs that are characteristic of COVID-19, such as ground-glass opacities and consolidation. This research work proposes an Intuitionistic fuzzy (IF) edge detector for the segmentation of COVID-19 CT images. Intuitionistic fuzzy sets go beyond conventional fuzzy sets by incorporating an additional parameter, referred to as the hesitation degree or non-membership degree. This extra parameter enhances the ability to represent uncertainty more intricately in expressing the degree to which an element may or may not belong to a set. The IF edge detector generates proficient results, when compared with the traditional edge detection algorithms and is validated in terms of performance metrics for benchmark images. Intuitionistic fuzzy edge detection has been shown to be effective in handling uncertainty and imprecision in edge detection.
边缘检测是医学图像处理的一个重要方面，在描绘图像中的边界和轮廓方面发挥着关键作用。这种能力对各种应用至关重要，包括医学成像领域的分割、特征提取和诊断程序。新冠肺炎是一种致命疾病，影响着世界上大多数国家的人们。新冠肺炎是由属于RNA病毒家族的冠状病毒引起的，会导致各种症状，如肺炎、发烧、呼吸困难和肺部感染。感兴趣区域（ROI）提取在疾病诊断和治疗中起着至关重要的作用。CT扫描有助于检测新冠肺炎特有的肺部异常，如磨玻璃影和实变。这项研究工作提出了一种用于新冠肺炎CT图像分割的直觉模糊（IF）边缘检测器。直觉模糊集通过纳入一个额外的参数（称为犹豫度或非隶属度）超越了传统模糊集。这个额外的参数增强了在表达元素可能属于或不属于一个集合的程度时更复杂地表示不确定性的能力。与传统边缘检测算法相比，IF边缘检测器产生了良好的结果，并根据基准图像的性能指标进行了验证。直觉模糊边缘检测已被证明在处理边缘检测中的不确定性和不精确性方面是有效的。
