The contour extraction problem with biomedical applications

Abstract

The problem of extracting a contour of some sort from noisy digitized images often arises within the context of the general pattern recognition problem. The approach presented here formulates the contour extraction problem as one of minimum cost tree searching. Branch costs or metrics are defined which are indicative of the likelihood that a particular branch lies on the true contour. The branch metrics incorporate both local and global or contextual information. The most likely path or contour is then extracted by application of a heuristic tree searching algorithm. In particular, use is made of the Zigangirov-Jelinek (ZJ) stack algorithm. This algorithm avoids the computational burden associated with structured or exhaustive search techniques and results in more manageable although random computational requirements. The approach is described first on an artificially contrived problem and then applied to two real biomedical image processing problems. The biomedical problems considered are concerned with the extraction of the left ventricular outline from serial angiocardiograms and the extraction of lung outlines from digitized chest X rays. It is demonstrated that the approach is fast, reliable, and versatile in the sense that it can be applied to a wide variety of contour extraction problems by appropriate definition of branch metrics.