USER-STEERED IMAGE SEGMENTATION PARADIGMS - LIVE WIRE AND LIVE LANE

In multidimensional image analysis, there are, and will continue to be , situations wherein automatic image segmentation methods fail, callin g for considerable user assistance in the process. The main goals of s egmentation research for such situations ought to be (i) to provide ef fective control to the user on the segmentation process while it is be ing executed, and (ii) to minimize the total user's time required in t he process. With these goals in mind, we present in this paper two par adigms, referred to as live wire and live lane, for practical image se gmentation in large applications. For both approaches, we think of the pixel vertices and oriented edges as forming a graph, assign a set of features to each oriented edge to characterize its ''boundariness,'' and transform feature values to costs. We provide training facilities and automatic optimal feature and transform selection methods so that these assignments can be made with consistent effectiveness in any app lication. In live wire, the user first selects an initial point on the boundary. For any subsequent point indicated by the cursor, an optima l path from the initial point to the current point is found and displa yed in real time. The user thus has a live wire on hand which is moved by moving the cursor, If the cursor goes close to the boundary, the l ive wire snaps onto the boundary. At this point, if the live wire desc ribes the boundary appropriately, the user deposits the cursor which n ow becomes the new starting point and the process continues. A few poi nts (live-wire segments) are usually adequate to segment the whole 2D boundary. in live lane, the user selects only the initial point. Subse quent points are selected automatically as the cursor is moved within a lane surrounding the boundary whose width changes as a function of t he speed and acceleration of cursor motion. Live-wire segments are gen erated and displayed in real time between successive points. The users get the feeling that the curve snaps onto the boundary as and while t hey roughly mark in the vicinity of the boundary. We describe formal e valuation studies to compare the utility of the new methods with that of manual tracing based on speed and repeatability of tracing and on d ata taken from a large ongoing application. The studies indicate that the new methods are statistically significantly more repeatable and 1. 5-2.5 times faster than manual tracing. (C) 1998 Academic Press.