Level set segmentation with outlier rejection

Geometric active contours based on edges perform poorly in the presence of noise or clutter. When the edges have gaps or are indistinct, the contour leaks through the boundary. Furthermore, when spurious edge points that do not belong to the object are present in the image, the contour is stopped by them and either does not converge to the object boundary or there is oversegmentation. This paper addresses the second difficulty. We propose a novel technique which classifies image features as valid or invalid making the curve stop only at valid features and allowing it to bridge the invalid ones. This is incorporated in the stopping force of boundary based level sets, achieving a robust contour estimation. Our algorithm organizes edge points into connected segments (denoted herein as strokes) and classifies each segment as valid or invalid. A confidence degree (weight) is assigned to each stroke and updated during the evolution process. Thus, the proposed stopping force is adaptive. Experimental results with real data will be provided to illustrate the performance of the proposed algorithm.