Live-wires using path-graphs.
OBJECTIVE
This article discusses our new Path-Graph approach for the interactive Live-Wire segmentation method in 2D applied to pre-segmented data. Furthermore, we examine whether or not the Live-Lane extension provides advantages in combination with pre-segmentation.


METHODS
We automatically over-segment the image data in a preprocessing step, using region growing with an automatic seed point generation. The Live-Wire algorithm is applied on this mosaic data by using the outlines of the homogeneous regions as the basis for graph building. We present a new definition of this underlying graph where the edges of the standard graphs are turning into vertices and the vertices of the new graph are defined by the edge connectivity in the standard graph. For better differentiation we name our new graph Path-Graph and the original defined graph Node-Graph.


RESULTS
The quality evaluation is done by comparing our segmentation results with existing model data. We show that using the Path-Graph as basis for the Live-Wire algorithm instead of the Node-Graph allows for a finer segmentation. We achieve a reduction of incorrectly classified pixels by 20.66 per cent and a decrease of the mean boundary deviation by 11.61 per cent. Since savings on cost tree calculations are compensated by additional computation time required to compute the Live-Lanes, a performance loss of 2.41 per cent is measured.


CONCLUSIONS
Our redefinition of the underlying graph increases the quality of the Live-Wire segmentation. The Live-Lane extension in combination with pre-segmentation is not justified for our data.
König
S
S
Institute for Computational Medicine, Universities of Mannheim and Heidelberg, Germany skoenig@rumms.uni-mannheim.de
Hesser
J
J
D000465:Algorithms; D003196:Computer Graphics; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging; D008491:Medical Informatics Applications; D012984:Software