  Active contours Model (ACM) has been extensively used in computer vision and
image processing. In recent studies, Convolutional Neural Networks (CNNs) have
been combined with active contours replacing the user in the process of contour
evolution and image segmentation to eliminate limitations associated with ACM's
dependence on parameters of the energy functional and initialization. However,
prior works did not aim for automatic initialization which is addressed here.
In addition to manual initialization, current methods are highly sensitive to
initial location and fail to delineate borders accurately. We propose a fully
automatic image segmentation method to address problems of manual
initialization, insufficient capture range, and poor convergence to boundaries,
in addition to the problem of assignment of energy functional parameters. We
train two CNNs, which predict active contour weighting parameters and generate
a ground truth mask to extract Distance Transform (DT) and an initialization
circle. Distance transform is used to form a vector field pointing from each
pixel of the image towards the closest point on the boundary, the size of which
is equal to the Euclidean distance map. We evaluate our method on four publicly
available datasets including two building instance segmentation datasets,
Vaihingen and Bing huts, and two mammography image datasets, INBreast and
DDSM-BCRP. Our approach outperforms latest research by 0.59 ans 2.39 percent in
mean Intersection-over-Union (mIoU), 7.38 and 8.62 percent in Boundary F-score
(BoundF) for Vaihingen and Bing huts datasets, respectively. Dice similarity
coefficient for the INBreast and DDSM-BCRP datasets is 94.23% and 90.89%,
respectively indicating our method is comparable to state-of-the-art
frameworks.
