Intellectual System of Preparation of Images from Computer Tomographs

Abstract

Artificial neural networks can be trained on useful signals of the source data, but can not be taught on noisy data, so it is usually performed noise reduction or error compensation. This paper implements a noise reduction model based on artificial neural networks to suppress high-noise components, which is important for optimizing pre-filtering methods. The process of cleaning computers’ tomography scans in medical examinations of patients with tuberculosis is considered as an given problem in which the suppression of noise present in the image is required.. In order to reduce the level of radiation due to it is quite harmful to human. the power of the radiation is reduced. As a result, the ratio of the useful signal to noise is reduced, which causes noise, which contaminates the image and complicates its processing. Additional shadows appears on the image that no objects exist, which can provide false diagnosis. An algorithm for structural-parametric synthesis of convolutional neural networks used in image noise suppression has been developed. Computer tomograms of tuberculosis patients provided by the Research Institute of Pulmonology and Tuberculosis of the National Academy of Medical Sciences of Ukraine were used as a training sample.