Developing a 3D Segmentation Technique by a Region-Growing Algorithm of CT Scan Lung Images

Conventional methodologies for lung image segmentation (LIS) encounter challenges posed by anatomical intricacies and intensity fluctuations in computed tomography (CT) scans.This study introduces a precise and effective approach to segmenting lung areas by utilising a region-growing algorithm.Initial steps involve data pre-processing, encompassing intensity regulation, noise reduction, and identification of lung regions.The core segmentation employs a region-growing algorithm; namely, active contours (ACs); with explicit criteria based on homogeneity, intensity values, and spatial connectivity.This iterative algorithm expands connected regions from seed points (SPs) within the identified lung region, ensuring conformity to defined criteria.Refinement of the segmentation occurs through the merging of neighbouring regions exhibiting similar attributes.Evaluation on a dataset of 196 chronic obstructive pulmonary disease (COPD) patients with varying degrees of lung abnormalities demonstrates accurate three-dimensional (3D) segmentation, yielding an average dice similarity coefficient (DSC) of 0.946 ± 0.023.This performance significantly surpasses that of thresholding methods (DSC: 0.826 ± 0.033), indicating a notably enhanced overlap between segmented lung areas and ground truth data.This study contributes a robust and efficient technique to the realm of LIS, facilitating precise 3D LIS in CT scans.