Many therapeutic and diagnostic procedures typically involve the use of CT imaging. When direct measurements of quality are required to analyze various patients kinds' signs and symptoms, location of the person's residence, and travel history, including close contact with any Covid-19 patient, it is the most effective and efficient tool for covid-19 diagnostic and therapeutic techniques. The segmentation procedure is crucial to CT diagnostic imaging. However, it can be difficult to tell the difference between symptoms and chest-related problems in pulmonary diagnostic CT imaging. The approach to decrease false positives for covid-19 recognition and to obtain useful structural data for lung diagnosis was proposed in this paper and experimentally tested. The first step is to segment the lungs of the interesting item using the morphological operation and thresholding approach of Otsu. Second, the 3D morphological feature-based parameters for covid-19 detection are measured. False positive reduction uses innovative SAV ratio-based thresholding as well as volume-based thresholding to remove undesired disturbances. The output identified significant structural data for the diagnosis of the lungs, (surface-area-to-volume ratio, volume, location, and density of covid-19 effect). Ten patients with covid-19 instances have their digitized transverse abdominal CT scans statistically examined and validated. To obtain validated data for the analysis, the expert radiologists separately estimated the coordinate points in the covid-19 region. With 95% overall accuracy, the suggested method greatly decreased false positives. Additionally, it can convey important details about the lung region affected by COVID-19.