Chest X-ray Image View Classification

Abstract

The view information of a chest X-ray (CXR), such as frontal or lateral, is valuable in computer aided diagnosis (CAD) of CXRs. For example, it helps for the selection of atlas models for automatic lung segmentation. However, very often, the image header does not provide such information. In this paper, we present a new method for classifying a CXR into two categories: frontal view vs. lateral view. The method consists of three major components: image pre-processing, feature extraction, and classification. The features we selected are image profile, body size ratio, pyramid of histograms of orientation gradients, and our newly developed contour-based shape descriptor. The method was tested on a large (more than 8,200 images) CXR dataset hosted by the National Library of Medicine. The very high classification accuracy (over 99% for 10-fold cross validation) demonstrates the effectiveness of the proposed method.