Medical image segmentation: Automated design of border detection criteria from examples

This paper provides examples of several medical image analysis applications for which single-purpose border detection approaches were developed in the past However, the utility of these and other existing automated and semiautomated medical image analysis systems is limited by their narrow, frequently single-purpose orientation. After a general approach to graph-based optimal border detection is overviewed, a new method for design of image segmentation systems is reported, in which the criterion of optimality is automatically determined by learning from border tracing examples. Border features employed in the designed method are selected from a predefined global set using radial-basis neural networks. The method was validated in intracardiac intravascular, and ovarian ultrasound images. The achieved performance was comparable to that of our previously reported single-purpose border detection methods. Our approach facilitates development of general multi-purpose image segmentation systems that can be trained for different types of image segmentation applications. (C) 1999 SPIE and IS&T. [S1017-9909(99)01201-5].