Extraction of objects from biomedical images is the fundamental task for many high level applications in medical image processing such as cytometry or diagnostic decision support. Therefore, a formal specification of sought objects is required along with an extraction procedure. On the basis of a hierarchical image decomposition objects
are described by image regions of characteristic shape, texture, and visual context. For example, a cell consists of a circular core, a surrounding body containing organelles, which is in turn surrounded by the nutrition agent, and other cells. This is modeled by hierarchical graph representation of the region topology as nodes and the region properties as node attributes. In a hierarchical region representation, an object is described by subregions which again may contain subregions, thus object extraction becomes the matching of the respective region nodes. Obviously, graph matching is a NP-complete problem and therefore, it requires heuristics to become computable.
This even holds for subtree matching. We propose a new approach which makes strongly use of the inclusion property of regions in a hierarchical image decomposition along with the visually descriptive attributes. The algorithm iterates a top-down bottom-up sequence over the region hierarchy to restrict the search space. Hence at each step, a layer of tree-node attributes must be compared to the attributes of the sought objects root node description. The bottom-up analysis is only invoked for the subtree depending on those nodes. Thus, each node is visited according to the topology of its visual occurrence in an image.
The fast visualisation of cerebral microcirculation supports diagnosis of acute cerebrovascular diseases. However, the commonly used CT/MRI-based methods are time consuming and, moreover, costly. Therefore we propose an alternative approach to brain perfusion imaging by means of ultrasonography. In spite of the low signal/noise-ratio of transcranial ultrasound and the high impedance of the skull, flow images of cerebral blood flow can be derived by capturing the kinetics of appropriate contrast agents by harmonic ultrasound image sequences. In this paper we propose three different methods for human brain perfusion imaging, each of which yielding flow images indicating the status of the patient's cerebral microcirculation by visualising local flow parameters. Bolus harmonic imaging (BHI) displays the flow kinetics of bolus injections, while replenishment (RHI) and diminution harmonic imaging (DHI) compute flow characteristics from contrast agent
continuous infusions. RHI measures the contrast agents kinetics in the influx phase and DHI displays the diminution kinetics of the contrast agent acquired from the decay phase. In clinical studies, BHI- and RHI-parameter images were found to represent comprehensive and reproducible distributions of physiological cerebral blood flow. For DHI it is shown, that bubble destruction and hence perfusion phenomena principally can be displayed. Generally, perfusion harmonic imaging enables reliable and fast bedside imaging of human brain perfusion. Due to its cost efficiency it complements cerebrovascular diagnostics by established CT/MRI-based methods.
Reliable automated analysis and examination of biomedical images
requires reproducible and robust extraction of contained image
objects. However, the necessary description of image content as
visually relevant objects is context-dependent and determined by
parameters such as resolution, orientation, and, of course, the
clinical-diagnostic question. Therefore a computer-based approach has
to model both examination context and image acquisition as expert
knowledge. Generally, static solutions are not satisfying because a
change of application will most likely require a redesign of the
analysis process. In contrast to non-satisfying statical solution,
this paper describes a flexible approach, which allows medical
examiners the context-sensitive extraction of sought objects
from almost arbitrary medical images, without requiring technical
knowledge on image analysis and processing. Since this methodology
is applicable to any analysis task on large image sets, it works for
general image series analysis as well as image retrieval. The new
approach combines classical image analysis with the idea of data mining to close the gap between low abstraction on the technical level and high-level expert knowledge on image content and understanding.
Today's solid state flat panel radiography detectors provide images which contain artifacts caused by lines, columns and clusters of inactive pixels. If not too large, such defects can be filled by interpolation algorithms which usually work in the spatial domain. This paper describes an alternative spectral domain approach to defect interpolation. The acquired radiograph is modeled as the undistorted image multiplied by a known binary defect window. The window effect is then removed by deconvolving the window spectrum from the spectrum of the observed, distorted radiograph. The basic ingredient of our interpolation algorithm is an earlier approach to block transform coding of arbitrarily shaped image segments, that extrapolates the segment internal intensities over a block into which the segment is embedded. For defect interpolation, the arbitrarily shaped segment is formed by a local image region with defects, thus turning extrapolation into defect interpolation. Our algorithm reconstructs both oriented structures and noise- like information in a natural-looking manner, even for large defects. Moreover, our concept can also be applied to non- binary defect windows, e.g. for gain correction.
The multiscale approach derives a segmentation from the evolution of appropriate signal-descriptive features in scale-space. Features that are stable for a wide range of scales are assumed to belong to visually sensible regions. To compensate the well-known drawbacks of linear scale- spaces, the shape-preserving properties of morphological scale-space filtering are utilized. The limiting duality of morphological filters is overcome by a selfdual morphological approach considering both light and dark structures in either the opening or the closing branch of the scale-space. Reconstructive opening/closing-filters enable the scale=analysis of 2D signals, since they are causal with respect to regional maxima/minima. This allows to identify important regions in scale=space via their extrema. Each extremum is assigned a region by a gradient watershed of the corresponding scale. Due to morphological filtering, the scale behavior of the regions is representable by a tree structure describing the spatial inter- and intra-scale relations among regions. The significance of a watershed region is automatically derived from its scale behavior by considering various attributes describing scale-dependent, morphological, and statistical properties of the region. The most significant regions from the segmentation of the image. The algorithm was verified for various medical image domains, such as cytological micrographs, bone x-rays, and cranial NMR slices.
KEYWORDS: Nonlinear filtering, Digital filtering, Image filtering, Signal to noise ratio, Interference (communication), Ultrasonography, Speckle, Electronic filtering, Linear filtering, Denoising
The quality of ultrasound images is limited due to granular speckle noise. The presented despeckle algorithm compensates the depth-dependent shape of granular speckles in sector scans by an initial coordinate transform. This yields a horizontally oriented speckle pattern of constant resolution and hence allows the use of constant filter templates. The signal-dependent nature of multiplicative speckle noise is considered by a ratio pyramid containing noise-normalized, subsampled scales corrupted by signal- independent noise. Since speckles can be identified as positive and negative impulses on the subsampled scales, they are removed by selfdual nonlinear multistage filters (NMF). The templates are adapted to the granular appearance of the speckles and the degree of filtering is individually controlled by the local noise power in each scale. We propose a new selfdual morphological pyramid with the common erosion/dilation as analysis operators and the reconstructive dilation/erosion as synthesis operators. The resulting closing-by-reconstruction and opening-by- reconstruction branches consider local intensity amplifications and attenuations, respectively. They are generated separately and combined only for scale-selective restoration by NMFs. Besides morphological decomposition, a ratio Laplacian pyramid is evaluated and its performance is compared with the proposed morphological decomposition, a ratio Laplacian pyramid is evaluated and its performance is compared with the proposed morphological decomposition. Both methods lead to significant noise reduction, where the morphological method introduces less signal degenerations.
Shape analysis of light-micrographs of cell populations is important for cytotoxicity evolution. This paper presents a morphological method for quantitative analysis of shape deformations of cells in contact to a biomaterial. After illumination normalization, a morphological multiscale segmentation yields separated cells. Shape deformation, and hence, toxicity of the substance under scrutiny, is quantified by means of compactness distribution and pattern spectrum of the population. Since the logarithmic image model is applicable to transmitted light, illumination normalization is achieved by removing the illumination component from the log- image by a tophat transform utilizing a large reconstruction filter. Subsequent thresholding and noise filtering yields connected binary cells, which are segmented by a marker-based, multiscale approach. For this, size-specific marker scales are generated removing noise and false markers. Each cell is now represented by an isolated marker. Converse integration of marker scales is performed by successive reconstruction of the original cell shapes, preventing merging of markers. Our method yields reasonable cell segmentations that go along with cell morphology even for differently sized and very distinct shapes. The obtained quantitative data is significantly correlated to the toxicity of the substance to be evaluated. Currently, the method is used for extensive biocompatibility tests.
The quality of ultrasound images is limited by granular speckle noise. This paper presents two nonlinear restoration methods based on multiscale signal decomposition. Initially, signal-dependent multiplicative speckle noise is transformed to additive noise by a logarithm point operation. Rectangular coordinates are obtained by a polar coordinate transform of the sector image. The lateral distortions require filter masks that are locally adapted to the ellipsoidal speckle spots. The images are decomposed into frequency bands and morphological scales by a Laplacian pyramid and self-dual morphological filtering, respectively. In both cases the subbands are filtered by special rank-order/morphological filters depending on lateral and radial resolutions of the ultrasound image. In case of Laplacian subbands, multistage filters consider elongated structures by unidirectional median filtering and subsequent rank-order operations. Morphological scales contain size-dependent speckle-shaped objects and are filtered by a novel self-dual reconstruction operator that equally treats noise resulting from amplified and attenuated reflected sound- waves. The performance of the despeckle algorithms is demonstrated for different types of B-mode sector scans. Both methods show significant noise reduction capability preserving object contours due to nonlinear filtering of the subbands.
To segment complex and versatile image data from different modalities it is almost impossible to achieve satisfying results without the consideration of contextual information. In this approach, image segmentation is regarded as a high- dimensional optimization task, that can be solved by stochastical methods like evolutionary algorithms (EA). Initially, the iterative algorithm is provided with a set of good-quality sample segmentations. An efficient EA-based learning strategy generates a segmentation for a given target image from the provided samples. This two-level process consists of a global image-based optimization whose convergence is enhanced by locally operating pixel-based Boltzmann processes which restrict the search space to reasonable subsets. The stochastic reconstruction extracts the relevant information from the samples in order to adapt it onto the current segmentation problem, which results in a consistent labeling for the target image. The algorithm works unsupervised, because the range of possible labels and their contextual interpretation is provided implicitly by the sample segmentations. To prove the usefulness of the method experimental results based on both, reproducible phantom images and physiological NMR scans are presented. Moreover, an analysis of the basic segmentation and convergence properties is provided.
In clinical cytology quantitative parameters have to be extracted from a large number of biological samples to obtain diagnostically relevant and reproducible information. Computer-assisted microscopy can provide methods that increase the quality and comparability of clinical studies by reducing the subjective influence of human operators on their results. In order to guarantee the correctness of extracted parameters automatic and reliable segmentation of the samples is required. For the detection of cytological objects a novel deformable membrane model is presented which is strictly based on macroscopical mechanics and statics. This is appropriate for modeling physiological membranes, because their shape is determined exclusively by mechanical forces. The self-driven membrane converges iteratively towards a stable state, where the contrary forces are in balance. However, active contours may not yield sufficient detection quality for acquisition of quantitative parameters. Therefore, after convergence a stochastic optimization process corrects the contour according to local graylevel information. This yields a contour that is well- adapted to the local graylevel structure. Additionally, for subsequent cytometric quantifications a local measure of confidence is provided for the contour. this can be used to enhance the robustness of the extracted parameters by incorporating the confidence factors in the quantification process. The method is applied to cytological and histological samples of different magnification.
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