Segmentation of Lung Images Using Textural Features

This paper proposes a technology for lung image segmentation using textural features. To analyze the informativeness of the features, the K-Means method was used. The segmentation result can be used to describe the characteristics of the patient: age, gender, physique, etc. The study was conducted on a large set of fragmented images. X-ray clustering errors for the 12×12, 24×24, and 36×36 fragmentation windows were presented. Pre-processing of images was also used to estimate the quality. The study showed that the technology provided key objects selecting error at little more than 25%. However, the alignment procedure reduced this error to 13%. When using the k-means method, a link was found between the segmentation error and the size of the fragmentation window. The selection of the range of interest on the lungs x-ray in accordance with the equalization was considered preferable. The high error of the first clustering method was associated with the choice of feature space; in our case, image processing was conducted for all features calculated using MaZda software.