The HRCT image analysis for quantitative description of peripheral airways remodelling

Airways remodelling is currently described as a process occurring before asthma becomes clinically manifest, which is confirmed by biopsy studies. The aim of this study was to test and validate image analysis methods to describe the changes such as peripheral airways remodelling in HRCT readings. Different methods of airways extraction from HRCT images were investigated including: manual identification of an airway region major axes on original and scaled images (using different interpolation techniques like pixel resize, bilinear interpolation and cubic convolution), manual extraction of the density profile through the major axes of an airway region, semi-automatic method using active contours and the Hough transform. Methods were tested with original images and artificially modified images by blurring and noise addition (Gaussian, Laplacian and salt-and-pepper). Results suggest that popular image magnification using cubic convolution is not suitable for accurate estimation of shape properties of small regions. Smart pixel resizing enables to delineate a region of inner and outer borders with subpixel accuracy reducing the total error of the wall thickness estimation. Additionally smoothing must be reduced to the minimum in the case of an active contour application.