Technical Report : OSU-CISRC-4/97-TR26, 1997

Segmentation of Medical Images Using LEGION

Naeem Shareef1, DeLiang L. Wang1,2, and Roni Yagel1
1Department of Computer and Information Science
2Center for Cognitive Science
The Ohio State University, Columbus, Ohio 43210, USA
{shareef, dwang, yagel}

Abstract.  Advances in visualization technology and specialized graphic workstations allow clinicians to virtually interact with anatomical structures contained within sampled medical image datasets. A hindrance to the effective use of this technology is the difficult problem of image segmentation. In this paper, we utilize a recently proposed oscillator network called LEGION (Locally Excitatory Globally Inhibitory Oscillator Network), whose ability to achieve fast synchrony with local excitation and desynchrony with global inhibition makes it an effective computational framework for grouping similar features and segregating dissimilar ones in an image. We identify four key computational components that determine its dynamics. Then, we describe an image segmentation algorithm that uses these LEGION components and an adaptive scheme to choose tolerances. We show results of the algorithm to 2D and 3D (volume) CT and MRI medical image datasets, and compare our results with manual segmentation. LEGION's computational and architectural properties make it a promising approach for real-time medical image segmentation using multiple features.

Technical Report: OSU-CIRC-4/97-TR26 (Postscript version)

Updated May 22, 1997