In this work, we address a very specific CAD (Computer Aided Detection/Diagnosis) problem and try to detect one of
the relatively common birth defects - spina bifida, in the prenatal period. To do this, fetal ultrasound images are used as
the input imaging modality, which is the most convenient so far. Our approach is to decide using two particular types of
views of the fetal neural tube. Transcerebellar head (i.e. brain) and transverse (axial) spine images are processed to
extract features which are then used to classify healthy (normal), suspicious (probably defective) and non-decidable
cases. Decisions raised by two independent classifiers may be individually treated, or if desired and data related to both
modalities are available, those decisions can be combined to keep matters more secure. Even more security can be
attained by using more than two modalities and base the final decision on all those potential classifiers.
Our current system relies on feature extraction from images for cases (for particular patients). The first step is image
preprocessing and segmentation to get rid of useless image pixels and represent the input in a more compact domain,
which is hopefully more representative for good classification performance. Next, a particular type of feature extraction,
which uses Zernike moments computed on either B/W or gray-scale image segments, is performed. The aim here is to
obtain values for indicative markers that signal the presence of spina bifida. Markers differ depending on the image
modality being used. Either shape or texture information captured by moments may propose useful features. Finally,
SVM is used to train classifiers to be used as decision makers. Our experimental results show that a promising CAD
system can be actualized for the specific purpose. On the other hand, the performance of such a system would highly
depend on the qualities of image preprocessing, segmentation, feature extraction and comprehensiveness of image data.
We propose a progressive mesh geometry coder, which expresses geometry information in terms of spectral coefficients obtained through a transformation and codes these coefficients using a hierarchical set partitioning algorithm. The spectral transformation used is the one proposed in  where the spectral coefficients are obtained by projecting the mesh geometry onto an orthonormal basis determined by mesh topology. The set partitioning method that jointly codes the zeroes of these coefficients, treats the spectral coefficients for each of the three spatial coordinates with the right
priority at all bit planes and realizes a truly embedded bitstream by implicit bit allocation. The experiments on common irregular meshes reveal that the distortion-rate performance of our coder is significantly superior to that of the spectral coder of .