SEGMENTATION OF MICROSCOPIC IMAGES OF SPUTUM COLORED BY THE ZIEHL-NEELSEN METHOD WITH THE USE OF ARTIFICIAL NEURAL NETWORKS
The research purpose was to study the development of an algorithm for preprocessing and segmentation of a digital image of a microscopic sputum preparation stained using the Ziehl-Neelsen method. For image segmentation, it is suggested to use the experimentally selected standard for each image of the pixel values in the HSV color model. During the research it was found that some of the boundary values are universal and do not change depending on the image and such parameters as the lower limits of H and S are individual for each image. The authors proposed to use the artificial multilayer neural networks to select these parameters. The results of the trained neural networks showed minimal differences selected neural networks of the boundary values of the selected limit values in manual mode under the control of a person's eyes. Based on the study results the algorithms for preprocessing and image segmentation are proposed and a block diagram of their operation is given. The proposed algorithms for preprocessing and image segmentation using neural networks can be used to further develop models for the identification of acid-fast bacilli and to study their characteristics which can be used in developing diagnostic systems for automated bacterioscopic diagnosis of tuberculosis.