Inter-Patient Modelling of 2D Lung Variations from Chest X-Ray Imaging via Fourier Descriptors

Detailed knowledge of anatomical lung variation is very important in medical image processing. Normal variations of lung consistent with the maintenance of pulmonary health and abnormal lung variations can be as a result of a pulmonary disease. Inter-patient lung variations can be due to the several factors such as sex, age, height, weight and type of disease. This study tries to show the inter-patient lung variations by using one of the shape-based descriptions techniques which is called Fourier descriptors. Shape-based description is an important approach to construct an object according to its parametric values. A different types of techniques are reported in the literature that aim to represent objects based on their shapes; each of these techniques has its cons and pros. Fourier descriptors, a simple yet powerful technique, has interesting properties such as rotational, scale, and translational invariance and these are powerful features for the recognition of two-dimensional connected shapes. In this paper, we use 380 CXR (Chest X-ray) images as a training set to construct the statistical mean model of lung contour. For modelling, the first step is evaluation of lung contour approximation and characterization to get the good spatial and frequency resolution. In the second step, all of the lung contours registered to show the variation and make a mean shape (i.e. lungs). And the final step is calculating the dispersion (i.e. covariance matrix) and analyzing by principle components. The proposed technique used to create the inter-patient statistical model and provide statistical parameters for application in segmentation, classification, 2D atlas based registration, etc. In this paper, we presented an approach for creating 2D modelling of human lungs from CXR image archives and reported some interesting statistical parameters to analysis the left and the right lung shape.