The paper presents an approach to the design of half-band discrete-time wavelets. This is accomplished through the use of a class of quadrature mirror filters which exhibit near- perfect reconstruction property. In particular, we present a technique for the design of such filters, wherein the designer has the flexibility to make tradeoffs between in- band behavior, out-of-band behavior, and the transition-band behavior. The basic formulation is carried out in the frequency domain, which is shown to translate the design problem into an eigenvalue-eigenvector problem. To find the optimal filter for a specific set of specifications, an optimization algorithm is also presented. Using this algorithm, designs ranging from 4 to 80 taps have been carried out successfully. A fairly complete table of resulting filters, which can be used by signal and image processing engineers, is included in the paper.
A neural-network-based algorithm is proposed for the restoration of nuclear medicine images as required for antibody therapy. The method was designed to address the particular problem of restoration of planar and tomographic bremsstrahlung data acquired with a gamma camera. Restoration was achieved by minimizing the energy function of the Hopfield network using a maximum entropy constraint. The performance of the proposed algorithm was tested on simulated data and planar gamma camera images of pure p-emitting radionuclides used in radioimmunotherapy. The results were compared with those of previously reported restoration techniques based on neural networks or traditional filters. Qualitative and quantitative analysis of the data suggested that the neural network with the maximum entropy constraint has good overall restoration performance; it is stable and robust even in cases where the signal-to-noise ratio is poor and scattering effects are significant. This behavior is particularly important in imaging therapeutic doses of pure β emitters such as yttrium-90 in order to provide accurate in vivo estimates of the radiation dose to the target and/or the critical organs.
The development of an extensive array of algorithms for both image enhancement and feature extraction for microcalcification cluster detection is reported. Specific emphasis is placed on image detail preservation and automatic or operator independent methods to enhance the sensitivity and specificity of detection and that should allow standardization of breast screening procedures. Image enhancement methods include both novel tree structured non-linear filters with fixed parameters and adaptive order statistic filters designed to further improve detail preservation. Novel feature extraction methods developed include both two channel tree structured wavelet transform and three channel quadrature mirror filter banks with multiresolution decomposition and reconstruction specifically tailored to extract MCC's. These methods were evaluated using fifteen representative digitized mammograms where similar sensitivity (true positive (TP) detection rate 100%) and specificity (0.1 - 0.2 average false positive (FP) MCC's/image) was observed but with varying degrees of detail preservation important for characterization of MCC's. The image enhancement step proved to be very critical to minimize image noise and associated FP detection rates for MCC's or individual microcalcifications.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.