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.
There has been significant research on pan-sharpening multispectral imagery with a high resolution image, but
there has been little work extending the procedure to high dimensional hyperspectral imagery. We present a
wavelet-based variational method for fusing a high resolution image and a hyperspectral image with an arbitrary
number of bands. To ensure that the fused image can be used for tasks such as classification and detection,
we explicitly enforce spectral coherence in the fusion process. This procedure produces images with both high
spatial and spectral quality. We demonstrate this procedure on several AVIRIS and HYDICE images.
Because hyperspectral imagery is generally low resolution, it is possible for one pixel in the image to contain
several materials. The process of determining the abundance of representative materials in a single pixel is called
spectral unmixing. We discuss the L1 unmixing model and fast computational approaches based on Bregman
iteration. We then use the unmixing information and Total Variation (TV) minimization to produce a higher
resolution hyperspectral image in which each pixel is driven towards a "pure" material. This method produces
images with higher visual quality and can be used to indicate the subpixel location of features.