A hybrid tool for data mining in picture archiving system

Abstract

A tool and a methodology for data mining in picture archiving systems are presented. It is intended to discover the relevant knowledge for picture analysis and diagnosis from the database of image descriptions. Knowledge engineering methods are used to obtain a list of attributes for symbolic image descriptions. An expert describes images according to this list, and stores descriptions in the database. Digital image processing can be applied to improve imaging of specific image features or to get expert-independent feature evaluation. Decision tree induction is used to learn the expert knowledge, presented in the form of image descriptions in the database. Constructed decision tree presents effective models of decision-making, which can be learned to support image classification by the expert. A tool for data mining and image processing is presented. The developed tool and methodology have been tested in the task of early differential diagnosis of pulmonary nodules in lung tomograms and was effective for preclinical diagnosis of peripheral lung cancer, so that we applied the developed methodology of data mining in other medical tasks such as lymph node diagnosis in MRI and investigation of breast MRI. © Springer-Verlag Berlin Heidelberg 2001.