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While geodesic active contours (GAC) have become very popular tools for image segmentation, they are sensitive
to model initialization. In order to get an accurate segmentation, the model typically needs to be initialized
very close to the true object boundary. Apart from accuracy, automated initialization of the objects of interest
is an important pre-requisite to being able to run the active contour model on very large images (such as those
found in digitized histopathology). A second limitation of GAC model is that the edge detector function is based
on gray scale gradients; color images typically being converted to gray scale prior to computing the gradient.
For color images, however, the gray scale gradient results in broken edges and weak boundaries, since the other
channels are not exploited for the gradient determination. In this paper we present a new geodesic active contour
model that is driven by an accurate and rapid object initialization scheme-weighted mean shift normalized cuts
(WNCut). WNCut draws its strength from the integration of two powerful segmentation strategies-mean shift
clustering and normalized cuts. WNCut involves first defining a color swatch (typically a few pixels) from the
object of interest. A multi-scale mean shift coupled normalized cuts algorithm then rapidly yields an initial
accurate detection of all objects in the scene corresponding to the colors in the swatch. This detection result
provides the initial boundary for GAC model. The edge-detector function of the GAC model employs a local
structure tensor based color gradient, obtained by calculating the local min/max variations contributed from each
color channel (e.g. R,G,B or H,S,V). Our color gradient based edge-detector function results in more prominent
boundaries compared to classical gray scale gradient based function. We evaluate segmentation results of our
new WNCut initialized color gradient based GAC (WNCut-CGAC) model against a popular region-based model
(Chan & Vese) on a total of 60 digitized histopathology images. Across a total of 60 images, the WNCut-CGAC
model yielded an average overlap, sensitivity, specificity, and positive predictive value of 73%, 83%, 97%, 84%,
compared to the Chan & Vese model which had corresponding values of 64%, 75%, 95%, 72%. The rapid and
accurate object initialization scheme (WNCut) and the color gradient make the WNCut-CGAC scheme, an ideal
segmentation tool for very large, color imagery.
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