Active contours with area-weighted binary flows for segmenting low SNR imagery

The detection of region boundaries in low SNR images using region based active contours is investigated. In this paper, we first show that the region based active contours models based on the region mean-difference binary flows suffer from two drawbacks when being applied to the detection of low SNR objects: (1) the contours give biased boundary locations if the image is smoothed with a linear filter; (2) the contours detect numerous false boundaries if the image is not prefiltered. Toward this end, we present a model that quantifies the contour location bias. Then, we derive a new region based model to detect the boundaries of objects in low SNR imagery, based on techniques of curve evolution, area-weighted mean-difference binary flows, and level sets. Finally, we present various experimental results to illustrate that the area-weighted technique can overcome the drawbacks of the existing method.