Automatic Extraction of Lung Boundaries by a Knowledge-Based Method

The aim of this paper is to develop accurate and reliable methods for automated detection of the edges of the lung by a knowledge-based approach. First, the system initialises the ROI(Region Of Interest) using 'unseeded region growing' algorithm. Then IPE(Image Processing Engine) generates candidates within the ROI. The candidates are matched to an anatomical model of the lung boundary using parametric features. A modular system architecture was developed which incorporates the model, image processing routines, an inference engine and a blackboard.

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