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.

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• INTRODUCTION
• LEGION MODEL
• COMPUTATIONAL COMPONENTS OF LEGION
• CONNECTED LABELLING USING LEGION
• G-LEGION For Binary Images
• Segmentation Algorithm For Grey Level Images
• RESULTS
• COMPARISON WITH MANUAL SEGMENTATION
• CONCLUDING REMARKS
• ACKNOWLEDGMENTS
• REFERENCES
• FIGURES

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