Recent research in perinatal pathology argues that analyzing properties of the placenta may reveal important
information on how certain diseases progress. One important property is the structure of the placental blood
vessels, which supply a fetus with all of its oxygen and nutrition. An essential step in the analysis of the vascular
network pattern is the extraction of the blood vessels, which has only been done manually through a costly
and time-consuming process. There is no existing method to automatically detect placental blood vessels; in
addition, the large variation in the shape, color, and texture of the placenta makes it difficult to apply standard
edge-detection algorithms. We describe a method to automatically detect and extract blood vessels from a given
image by using image processing techniques and neural networks. We evaluate several local features for every
pixel, in addition to a novel modification to an existing road detector. Pixels belonging to blood vessel regions
have recognizable responses; hence, we use an artificial neural network to identify the pattern of blood vessels.
A set of images where blood vessels are manually highlighted is used to train the network. We then apply
the neural network to recognize blood vessels in new images. The network is effective in capturing the most
prominent vascular structures of the placenta.
We introduce "contour stencils" as a simple method for detecting the local orientation of image contours and
apply this detection to image zooming. Our approach is motivated by the total variation along curves: small total
variation along a candidate curve suggests that this curve is a good approximation to the contours. Furthermore,
a relationship is shown between interpolation error and total variation. The contour stencil detection is used to
develop two image zooming methods. The first one, "contour stencil interpolation," is simple and computationally
efficient, yet competitive in a comparison against existing methods. The second method approaches zooming
as an inverse problem, using a graph regularization where the graph is determined by contour stencil detection.
Both methods extend naturally to vector-valued data and are demonstrated for grayscale and color images.
We first develop a simple method for detecting the local orientation of image contours and then use this detection
to design an edge-adaptive image interpolation strategy. The detection is based on total variation: small total
variation along a candidate curve implies that the image is approximately constant along that curve, which
suggests it is a good approximation to the contours. The proposed strategy is to measure the total variation over
a "contour stencil," a set of parallel curves localized over a small patch in the image. This contour stencil detection
is used to design an edge-adaptive image interpolation strategy. The interpolation is computationally efficient,
operates robustly over a variety of image features, and performs competitively in a comparison against existing
methods. The method extends readily to vector-valued data and is demonstrated for color image interpolation.
Other applications of contour stencils are also discussed.