An ultra-fast user-steered image segmentation paradigm: live-wire-on-the-fly

In the past, we have presented three user-steered image segmentation paradigms: live wire, live lane, and the 3D extension of the live-wire method. In this paper, we introduce an ultra-fast live-wire method, referred to as live-wire-on-the-fly, for further reducing user's time compared to live wire. For both approaches, given a slice and a 2D boundary of interest in this slice, we translate the problem of finding the best boundary segment between any two points specified by the user on this boundary to the problem of finding the minimum-cost path between two vertices in a weighted and directed graph. The entire 2D boundary is identified as a set of consecutive boundary segments, each specified and detected in this fashion. A drawback in live wire is that the speed for optimal path computation depends on image size, compromising the overall segmentation efficiency. In this work, we solve this problem by exploiting some properties of graph theory to avoid unnecessary minimum-cost path computation during segmentation. Based on 164 segmentation experiments from an actual medical application, we demonstrate that live-wire-on-the-fly is about 1.5 to 33 times faster than live wire for actual segmentation, although the pure computational part alone is found to be over a hundred times faster.