Mining lung shape from X-Ray images

Abstract

This paper presents an approach for mining 2D shape of human lungs from large x-ray image archives of a national level. Images were accumulated in framework of a compulsory computerized country-wide screening programme launched few years ago which is being under development. Three study groups of images containing about 21, 18 and 39 thousand of subjects were created by sub-sampling from a test database resulted from pulmonary x-ray examinations of a total of 188 thousands people. These groups have been well balanced by age and gender according to the existing biomedical standards and subsequently used as input data for searching different kinds of regularities in 2D projective lung shape and size. The approach followed in the paper combines different methods including procrustes shape analysis, Bookstein's baseline shape registration, multi-dimensional scaling, regression models with broken-line relationships as well as various conventional statistical procedures. As a result, interesting gender- and age-related regularities in lung shape were discovered and documented in the paper. © 2009 Springer Berlin Heidelberg.