In the Spotlight: Biomedical Imaging

This article reviews some of the more recent advances and trends in the area of biomedical imaging. Real-time multimodality imaging and image-guided interventions are presented as well as other fast growing areas of interdisciplinary research and development. Segmentation, registration and spatial-temporal integration in medical image processing are also discussed

Hexagonal QMF Banks and Wavelets

In this chapter we shall lay bare the theory and implementation details of hexagonal sampling systems and hexagonal quadrature mirror filters (HQMF). Hexagonal sampling systems are of particular interest because they exhibit the tightest packing of all regular two-dimensional sampling systems and for a circularly band-limited waveform, hexagonal sampling requires 13.4 percent fewer samples than rectangular sampling. In addition, hexagonal sampling systems also lead to nonseparable quadrature mirror filters in which all basis functions are localized in space, spatial frequency and orientation. This chapter is organized in two sections. Section I describes the theoretical aspects of hexagonal sampling systems while Section II covers important implementation details

Texture Classification by Wavelet Packet Signatures

This correspondence introduces a new approach to characterize textures at multiple scales. The performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classification of twenty-five natural textures. Both energy and entropy metrics were computed for each wavelet packet and incorporated into distinct scale space representations, where each wavelet packet (channel) reflected a specific scale and orientation sensitivity. Wavelet packet representations for twenty-five natural textures were classified without error by a simple two-layer network classifier. An analyzing function of large regularity (D20) was shown to be slightly more efficient in representation and discrimination than a similar function with fewer vanishing moments (D6) In addition, energy representations computed from the standard wavelet decomposition alone (17 features) provided classification without error for the twenty-five textures included in our study. The reliability exhibited by texture signatures based on wavelet packets analysis suggest that the multiresolution properties of such transforms are beneficial for accomplishing segmentation, classification and subtle discrimination of texture

Multiscale wavelet representations for mammographic feature analysis

This paper introduces a novel approach for accomplishing mammographic feature analysis through multiresolution representations. We show that efficient (nonredundant) representations may be identified from digital mammography and used to enhance specific mammographic features within a continuum of scale space. The multiresolution decomposition of wavelet transforms provides a natural hierarchy in which to embed an interactive paradigm for accomplishing scale space feature analysis. Choosing wavelets (or analyzing functions) that are simultaneously localized in both space and frequency, results in a powerful methodology for image analysis. Multiresolution and orientation selectivity, known biological mechanisms in primate vision, are ingrained in wavelet representations and inspire the techniques presented in this paper. Our approach includes local analysis of complete multiscale representations. Mammograms are reconstructed from wavelet coefficients, enhanced by linear, exponential and constant weight functions localized in scale space. By improving the visualization of breast pathology we can improve the changes of early detection of breast cancers (improve quality) while requiring less time to evaluate mammograms for most patients (lower costs)

Multiscale sub-octave wavelet transform for de-noising and enhancement

This paper describes an approach for accomplishing sub- octave wavelet analysis and its discrete implementation for noise reduction and feature enhancement. Sub-octave wavelet transforms allow us to more closely characterize features within distinct frequency bands. By dividing each octave into sub-octave components, we demonstrate a superior ability to capture transient activities in a signal or image more reliably. De-noising and enhancement are accomplished through techniques of minimizing noise energy and nonlinear processing of transform coefficient energy by gain

De-Noising via Wavelet Transforms Using Steerable Filters

Feature extraction remains an important part of low-level vision. Traditional oriented filters have been effective tools to identify features, such as lines and edges. Steerable filters, which can be adjusted at arbitrary orientation, have made decisions of feature orientations more precise. Combined with a pyramid structure of a multiscale representation, these filters can provide a reliable and efficient tool for image analysis. This paper takes advantage of multiscale steerable filters in the context of de-noising. First a set of novel filters are designed, that decompose the frequency plane into distinct directional bands. Next, we identify the dominant direction and strength at each point of an image from quadrature pairs of steerable filters. A nonlinear threshold function is then applied to the filtered coefficients to suppress noise. The denoised image is restored from coefficients modified at each level of transform space. We demonstrate the benefits of multiscale steerable filters for de-noising and show that it can greatly reduce noise while preserving image features. Two examples are presented to verify the efficacy of the technique

Coherence of Multiscale Features for Enhancement of Digital Mammograms

Mammograms depict most of the significant changes in breast disease. The primary radiographic signs of cancer are related to tumor mass, density, size, borders, and shape, and local distribution of calcifications. We show that each of these features can be well described by coherence and orientation measures and provide visual cues for radiologists to identify possible lesions more easily without increasing false positives. In this paper, an artifact-free enhancement algorithm based on overcomplete multiscale representations is presented. First, an image was decomposed using a fast wavelet transform algorithm. At each level of analysis, energy and phase information are computed via a set of separable steerable filters. Then, a measure of coherence within each level was obtained by weighting an energy measure with the ratio of projections of local energy within a specified window. Each projection was computed onto the central point of a window with respect to the total energy within that window. Finally, a nonlinear operation, integrating coherence and orientation information, was applied to modify transform coefficients within distinct levels of analysis. These modified coefficients were then reconstructed, via an inverse fast wavelet transform, resulting in an improved visualization of significant mammographic features. The novelty of this algorithm lies in the detection of directional multiscale features and the removal of aliased perturbations

A Parallel Algorithm for High-Speed Stereo Matching

The goal of stereo vision is the recovery of depth information from the relative lateral displacements in the positions of objects within a pair of images taken from slightly differing viewpoints. While recent stereo matching techniques have yielded improvements in reliability and speed, most of these algorithms fall short of the real-time stereo matching requirements for navigation systems, robot vision, machine inspection, and other areas computer vision where rapid response is critical. Traditionally, matching algorithms have achieved high speeds through algorithm simplification and/or relied on custom hardware. The objective of our work has been the develop of a robust high-speed stereo matcher by exploiting parallel algorithms executing on general purpose SIMD machines. Our approach is based on several existing techniques dealing with the classification and evaluation of matches, the application of ordering constraints, and relaxation-based matching. The techniques have bene integrated and reformulated in terms of parallel execution on a theoretical SIMD machine. An ideal machine topology for executing this parallel algorithm is identified through complexity analysis. Feasibility is demonstrated by implementation on a commercially available SIMD machine, and its performance compared with that of the idealized machine. Sample results are shown for real and synthetic stereo pairs

A Parallel Algorithm for Incremental Stereo Matching on SIMD Machines

Previous matching algorithms have achieved high speeds through algorithm simplification and/or relied on custom hardware. The objective of our work has been the development a robust high-speed stereo matcher by exploiting parallel algorithms executing on general purpose SIMD machines. Our approach is based on several existing techniques dealing with the classification and evaluation of matches, the application of ordering constraints, and relaxation-based matching. The techniques have been integrated and reformulated in terms of parallel execution on the theoretical SIMD machine. Feasibility is demonstrated by implementation on a commercially available SIMD machine. Its performance is compared with that of the idealized machine