3D Computerized Segmentation of Lung Volume With Computed Tomography

Three-dimensional (3D)-based detection and diagnosis has an important role for significantly improving the detection and diagnosis of lung cancer upon computed tomography (CT). This report presents a 3D-based method for segmenting and visualizing lung volume by using CT images. An anisotropic filtering method was developed on CT slices to enhance the signal-to-noise ratio, and a wavelet transform-based interpolation method was used combined with volume rendering to construct the 3D volumetric data based on entire CT slices. Then an adaptive 3D region-growing algorithm was designed to segment lung volume, incorporated by automatic seed-locating methods through fuzzy logic algorithms and 3D morphological closing approaches. In addition, a 3D visualization tool was designed to view volumetric data, projections, or intersections of the lung volume at any view angle. This segmentation method was tested on single-detector CT images by percentage of volume overlap and percentage of volume difference. The experiment results show that the developed 3D-based segmentation method is effective and robust. This study lays the groundwork for 3D-based computerized detection and diagnosis of lung cancer with CT imaging. In addition, this approach can be integrated into a picture archiving and communication system serving as a visualization tool for radiologists' reading and interpretation.