Fully automatic segmentation on medical images often generates unreliable results so we must rely on semi-automatic methods that use both user input and boundary refinement to produce a more accurate result. In this paper, we present an improved livewire method for noisy regions of interest with low contrast boundaries. The first improvement is the adaptive search space, which minimizes the required search area for graph generation, and a directional graph searching which also speeds up the shortest path finding. The second improvement is an enhanced cost function to consider only the local maximum gradient within our search area, which prevents interference from objects we are not interested in. The third improvement is the on-the-fly training based on gradient histogram to prevent attraction of the contour to strong edges that are not part of the actual contour. We carried out tests between the original and our improved version of livewire. The segmentation was validated on phantom images and also against manual segmentation defined by experts on uterine leiomyomas MRI. Our results show that, on average, our method reduces the time to completion by 96% with improved accuracy up to 63%.

Date Published: 26 March 2007
PDF: 11 pages
Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65122Z (26 March 2007); doi: 10.1117/12.709934

Published in SPIE Proceedings Vol. 6512:
Medical Imaging 2007: Image Processing
Josien P. W. Pluim; Joseph M. Reinhardt, Editor(s)