Extraction of pulmonary nodules in CT images based on 2DPCA with adaptive parameters

Two-dimensional Principal component analysis (2DPCA) is widely used in face feature extraction and recognition as its lower-computational complexity comparing with principal component analysis (PCA). In this paper, we propose a feature extraction algorithm of pulmonary nodules based on 2DPCA with adaptive parameters. The cumulative variance proportion which is the histogram peak value of CT image can be selected self-adaptively only by one lung CT image itself but not any human intervention or priori-information, which could aid the radiologists more effectively. The experiment shows that this algorithm has a better performance of feature extraction than (PCA) and 2DPCA.