37 research outputs found
The Computer System: Developing a Plan for the School District
Educators must develop a computer strategy to cover all potential needs of their district
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Hexagonal wavelet processing of digital mammography
This paper introduces a novel approach for accomplishing mammographic feature analysis through overcomplete multiresolution representations. We show that efficient representations may be identified from digital mammograms and used to enhance features of importance to mammography within a continuum of scale-space. We present a method of contrast enhancement based on an overcomplete, non-separable multiscale representation: the hexagonal wavelet transform. Mammograms are reconstructed from transform coefficients modified at one or more levels by local and global non-linear operators. Multiscale edges identified within distinct levels of transform space provide local support for enhancement. We demonstrate that features extracted from multiresolution representations can provide an adaptive mechanism for accomplishing local contrast enhancement. We suggest that multiscale detection and local enhancement of singularities may be effectively employed for the visualization of breast pathology without excessive noise amplification
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Adaptive multiscale processing for contrast enhancement
This paper introduces a novel approach for accomplishing mammographic feature analysis through overcomplete multiresolution representations. We show that efficient representations may be identified from digital mammograms within a continuum of scale space and used to enhance features of importance to mammography. Choosing analyzing functions that are well localized in both space and frequency, results in a powerful methodology for image analysis. We describe methods of contrast enhancement based on two overcomplete (redundant) multiscale representations: (1) Dyadic wavelet transform (2) (phi) -transform. Mammograms are reconstructed from transform coefficients modified at one or more levels by non-linear, logarithmic and constant scale-space weight functions. Multiscale edges identified within distinct levels of transform space provide a local support for enhancement throughout each decomposition. We demonstrate that features extracted from wavelet spaces can provide an adaptive mechanism for accomplishing local contrast enhancement. We suggest that multiscale detection and local enhancement of singularities may be effectively employed for the visualization of breast pathology without excessive noise amplification
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Comparison of a dyadic wavelet image enhancement algorithm with unsharp masking and median filtering for mammography
Image processing techniques using wavelet signal analysis techniques have shown promise in mammography. Wavelet algorithms are compared with traditional image enhancement techniques of unsharp masking and median filtering. Computer simulated phantom images were generated containing lesions mimicking masses and microcalcifications. The degree of image enhancement was evaluated by comparing processed and original signal-to-noise (SNR) ratios in such phantom images. Results obtained in this study suggest that image processing algorithms based on the wavelet transform are likely to enhance the visibility of low-contrast features in mammograms
The development, design, testing, refinement, simulation and application of an evaluation framework for communities of practice and social-professional networks
Background. Communities of practice and social-professional networks are generally considered to enhance workplace experience and enable organizational success. However, despite the remarkable growth in interest in the role of collaborating structures in a range of industries, there is a paucity of empirical research to support this view. Nor is there a convincing model for their systematic evaluation, despite the significant potential benefits in answering the core question: how well do groups of professionals work together and how could they be organised to work together more effectively? This research project will produce a rigorous evaluation methodology and deliver supporting tools for the benefit of researchers, policymakers, practitioners and consumers within the health system and other sectors. Given the prevalence and importance of communities of practice and social networks, and the extent of investments in them, this project represents a scientific innovation of national and international significance. Methods and design. Working in four conceptual phases the project will employ a combination of qualitative and quantitative methods to develop, design, field-test, refine and finalise an evaluation framework. Once available the framework will be used to evaluate simulated, and then later existing, health care communities of practice and social-professional networks to assess their effectiveness in achieving desired outcomes. Peak stakeholder groups have agreed to involve a wide range of members and participant organisations, and will facilitate access to various policy, managerial and clinical networks. Discussion. Given its scope and size, the project represents a valuable opportunity to achieve breakthroughs at two levels; firstly, by introducing novel and innovative aims and methods into the social research process and, secondly, through the resulting evaluation framework and tools. We anticipate valuable outcomes in the improved understanding of organisational performance and delivery of care. The project's wider appeal lies in transferring this understanding to other health jurisdictions and to other industries and sectors, both nationally and internationally. This means not merely publishing the results, but contextually interpreting them, and translating them to advance the knowledge base and enable widespread institutional and organisational application