Cellular processors in multichannel image classifiers

Abstract

The purpose of the work is to analyze multichannel images used in medical research related to the classification of radiographs. Classification rules for the bitmap multichannel images are based on two methods of the descriptors formation. Through these descriptors, two groups of classifiers are built with the subsequent aggregation of solutions. In channels with high image spatial resolution the descriptors are formed based on the analysis of border contours of the corresponding bitmap segments. To analyze and classify the selected contours, the bitmaps in channels with high resolution in the spatial frequency range or in the electromagnetic spectrum are used. The use of multiscale windows in each channel allows creating multiple classifiers for one channel with the subsequent aggregation of solutions both within the channel and between the channels. This results in a network structure of classifiers (cellular classifiers), which parameters are determined through training, based on expert assessments or hybrid methods. The result of the research is the development of efficient algorithms for processing and analyzing multichannel images. The authors determine the models’ structure based on cellular processors using neural networks. Those structures can be adapted to specific features of the image and allow implementing the objects’ classification in medical images in real time. The conclusions are drawn about the possibility of applying the method to building an intelligent decision‐making system for all types of processed multichannel bitmap images.