Determination of a Similar Anatomical Area on a Chest CT Image Using Traditional Image Feature Extraction Methods

Abstract

The traditional image descriptor definition algorithms are considered, such as SIFT, ORB, LBP, GLSM. With the help of them, the searching task for a similar anatomical area on the CT images of the lungs is solved. The article proposes a methodology for performing a comparative traditional algorithms for determining images descriptors analysis and optimal anatomical features. Algorithms are tested when searching for a similar anatomical layer in the framework of the computer tomography images layers of of light patient, as part of the search for similar anatomical form on the layer among the computer tomography images of light two patients, and among the images of computed tomography of light hundred patients. As a result, it is determined that GLSM shows the best results when solving the task of classifying an image anatomical area (averaged error of determining the anatomical layer is 5 %). It is determined that the optimal signs on the lungs correspond to the presence of organs: heart, liver and top edge of the lung. Conclusions are fomulated about the need to use neural network methods to improve the error in determining the similar layer containing the necessary anatomical structure.

Keywords

• sift, orb, lbp, glsm, computed tomography of lung, similar anatomical area, image classification, traditional descriptor definition algorithms, searching for a similar clinical case