Eigen nodule: view-based recognition of lung nodule in chest X-ray CT images using subspace method

Abstract

We previously proposed a recognition method of lung nodules based on the experimentally selected feature values (such as contrast, circularities, etc.) of pathological candidate regions detected by our Quoit filter. In this paper, we propose a new recognition method of lung nodule using each CT value itself in ROI (region of interest) area as a feature value. In the clustering stage, the pathological candidate regions are first classified into some clusters using the principal component (PC) theories. A set of CT values in each ROI is regarded as a feature vector, and then eigen vectors and eigen values are calculated for each cluster by applying the principal component analysis (PCA). The eigen vectors (we call them eigen images) corresponding to the 10 largest eigen values, are utilized as base vectors for subspaces of the clusters in the feature space. In the discrimination stage, correlations are measured between the testing feature vector and the subspace which is spanned by the eigen images. If the correlation with the abnormal subspace is large, the pathological candidate region is determined to be abnormal. Otherwise, it is determined to be normal. By applying our new method, good results have been acquired.