Model-based assessment of lung structures: inferring and control system

摘要

A general methodology has been developed for computer interpretation of medical images, based on an explicit anatomical model. A test system for analyzing posterior- anterior (PA) chest x-rays has been implemented. The inferencing and control system identifies the major lung structures in the image, and then flags any suspected abnormalities. Image and model data are transformed into a feature space where they are represented in terms of edge descriptions. The inference engine compares the image and model in feature space to label the edges anatomically, and check for normality. The control system schedules events within the inference engine and coordinates interaction with the model and image processing routines. The control architecture is blackboard-based, with a separate data frame for each structure to be identified. The anatomical model uses fuzzy sets to provide ranges of feature values which are considered normal or indicative of a particular abnormality. This allows the inference engine to give a confidence score and linguistic description to each decision. Mediastinum, cardiac border, domes of the diaphragm, ribs and lung outline have been modeled. Their automatic identification allows diagnostic checks such as the cardiothoracic ratio, comparison of right and left lungs to identify lobular collapse and inspection of interfaces in terms of shape and clarity. The inference engine provides simple comments on its findings, making it suitable for pre- and double-checking of images.