Image processing and syndrome feature analysis to improve image interpretation

Abstract

A series of methods have been developed, which are to support medical image analysis and interpretation in the case of data uncertainty and the lack of information for reliable and correct image interpretation. The methods allow the better representation of informative features for expert's analysis; they use the expert's domain knowledge and help to recover new knowledge from the database of processed image descriptions. Created formal decision rules for image analysis and interpretation imitate human argumentation, and present the solution in easily interpretable form. Developed methods have been applied for early peripheral lung cancer diagnosis. Their use helped to enhance expert's diagnostic abilities and essentially improved results of medical image analysis and decision-making for the experts of different qualifications. Developed methods were useful for correct diagnosis of small radiographically indeterminate pulmonary opacities. They supported expert's interpretation of conventional and computed tomography images in the case of uncertainty.