| Summary: | Lung lesion identification is an important aspect of an early lung cancer diagnosis. Early identification of lung cancer may assist physicians in treating patients. This paper uses computed tomography scan images to present a lung lesion identification geometrical feature. From the previous studies, lung segmentation is particularly challenging because differences in pulmonary inflation with an elastic chest wall can result in significant variability in volumes and margins when attempting to automate lung segmentation. Besides, the features used to describe a lung lesion focus on image features which are geometric, appearance, texture, and others. This study develops an image processing technique that uses image segmentation algorithms to segment lung lesions in computed tomography images. The suggested approach includes the following stages, which require image processing techniques: data collection, image segmentation, and performance evaluation. The computed tomography scan images were collected from Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia database. As a contribution to biomedical engineering, this study has successfully calculated the performance of the image processing method for lung segmentation, which gets an average accuracy of 99.38%, recall is 99.45%, and F-score is 99.6. The lung lesion segmentation approach based on the object's size could help investigate image abnormality for medical analysis. From the study, 80% of the total lesion identification using the proposed method was correctly predicted when compared with the radiologist's lesion mark. The experiment results found clear support for the next stage of this research. © 2023 Penerbit UTM Press. All rights reserved.
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