Integration of neural networks and rule based systems in the interpretation of liver biopsy images

Abstract

Treatment of natural images requires, due to their complexity, to exploit high level knowledge, such as domain knowledge and heuristics, which are typically well formalized by rule based systems. However, the intrinsic variability and irregularity of objects in the image makes their characterization in terms of rules often unfeasible. Such variability and irregularity are, on the other hand, the ultimate reason for the existence of statistical methods. For these reasons, a hybrid system, exploiting characteristics of both approaches, may show better performances than purely syntactical or statistical systems in the interpretation of natural images. In this paper we present a hybrid system for image interpretation that integrates a rule based system with a Labeled Learning Vector Quantizer. The rule based system controls the interpretation process, by dynamically determining the interpretation strategy, and the Labeled Learning Vector Quantizer is exploited as classification kernel. The system has been tested on images of liver biopsies. Results on nuclei classification are here discussed.