Nonlinear processing and semantic content analysis in medical imaging-a cognitive approach

The traditional approach to automated analysis of medical data is mostly based on statistical or theoretical-decision methods of pattern recognition. Using such methods, we can obtain many valuable items, e.g., tracking patients' movements, control of medical treatment, etc. In medical informatics there are still many problems not solvable by computers and reserved for human medical staff. Such problems can be solved by the development of scientific research toward machine intelligence. In this paper, we try to show how the computer can understand medical data instead of simple processing and analysis. Sometimes it may be useful to make semantic content analysis leading to automatic understanding of medical data, e.g., for intelligent help in the diagnosis process or in semantics-based searching through medical databases. This paper will present the application of the cognitive-based approach for intelligent semantic analysis, allowing describing automatically important diagnostic features of analyzed images. This approach is based on a special kind of image description language and grammar formalism. During the linguistic analysis of medical patterns, we can solve the problem of generalization of features of a selected image and obtaining semantic content description of the image. The most important part of this analysis depends on the "cognitive resonance" process, in which features of real images are compared with some kind of expectation taken from the knowledge base containing knowledge regarding pathological cases originating from medical practice.