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Results for “scale-space”

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At least 19 recordsLinked to original sources

PubMed2001

Evaluation of using absolute versus relative base level when analyzing brain activation images using the scale-space primal sketch.

A dominant approach to brain mapping is to define functional regions in the brain by analyzing images of brain activation obtained from positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). This paper presents an evaluation of using one such tool, called the scale-space primal sketch, for brain activation analysis. A comparison is made concerning two possible definitions of a significance measure of blob structures in scale-space, where local contrast is measured either relative to a local or global reference level. Experiments on real brain data show that (i) the global approach with absolute base level has a higher degree of correspondence to a traditional statistical method than a local approach with relative base level, and that (ii) the global approach with absolute base level gives a higher significance to small blobs that are superimposed on larger scale structures, whereas the significance of isolated blobs largely remains unaffected. Relative to previously reported works, the following two technical improvements are also presented. (i) A post-processing tool is introduced for merging blobs that are multiple responses to image structures. This simplifies automated analysis from the scale-space primal sketch. (ii) A new approach is introduced for scale-space normalization of the significance measure, by collecting reference statistics of residual noise images obtained from the general linear model.

Algorithms↗

PubMed1999

Analysis of brain activation patterns using a 3-D scale-space primal sketch.

A fundamental problem in brain imaging concerns how to define functional areas consisting of neurons that are activated together as populations. We propose that this issue can be ideally addressed by a computer vision tool referred to as the scale-space primal sketch. This concept has the attractive properties that it allows for automatic and simultaneous extraction of the spatial extent and the significance of regions with locally high activity. In addition, a hierarchical nested tree structure of activated regions and subregions is obtained. The subject in this article is to show how the scale-space primal sketch can be used for automatic determination of the spatial extent and the significance of rCBF changes. Experiments show the result of applying this approach to functional PET data, including a preliminary comparison with two more traditional clustering techniques. Compared to previous approaches, the method overcomes the limitations of performing the analysis at a single scale or assuming specific models of the data.

Brain Mapping↗

PubMed1997

The curvature function evolved in scale-space as a representation of biological shapes.

A new and simple approach for calculating the curvature functions of arbitrary non-analytical (biological) contours of planar shapes is described. This approach does not require either parametrization or the evaluation of derivatives, which eliminates sources of error and noise amplification. It is shown that this method provides better results than the traditional procedure and the procedure employing polar coordinates. The paper describes how to evolve contours in scale-space, and it is discussed how, due to its invariant properties, curvature functions, evolved in scale-space, are suitable as representations of shapes. The techniques described are applied to the digitized contours of isolated corneal endothelial cells.

Cell Size↗

PubMed1999

Scale-space signatures for the detection of clustered microcalculations in digital mammograms.

A method is described for the automated detection of microcalcifications in digitized mammograms. The method is based on the Laplacian scale-space representation of the mammogram only. First, possible locations of microcalcifications are identified as local maxima in the filtered image on a range of scales. For each finding, the size and local contrast is estimated, based on the Laplacian response denoted as the scale-space signature. A finding is marked as a microcalcification if the estimated contrast is larger than a predefined threshold which depends on the size of the finding. It is shown that the signature has a characteristic peak, revealing the corresponding image features. This peak can be robustly determined. The basic method is significantly improved by consideration of the statistical variation of the estimated contrast, which is the result of the complex noise characteristic of the mammograms. The method is evaluated with the Nijmegen database and compared to other methods using these mammograms. Results are presented as the free-response receiver operating characteristic (FROC) performance. At a rate of one false positive cluster per image the method reaches a sensitivity of 0.84, which is comparable to the best results achieved so far.

Algorithms↗

PubMed1999

Removal of extracerebral tissues in dual-echo magnetic resonance images via linear scale-space features.

The location and masking of the brain and surrounding cerebrospinal fluid (CSF) in two-dimensional (2D) dual-echo fast spin-echo (FSE) magnetic resonance (MR) images of the head is achieved by an automated procedure with a voxel-based computational algorithm. Linear scale-space features are derived from the short-echo, proton-density (PD)-weighted images. The second-order Gaussian derivative (the Laplacian) operator is applied at three different spatial scales as a measure of image convexity/concavity with a first-order Gaussian derivative measure (the squared gradient) at a single scale used to circumscribe cortical regions. A mask obtained from the long-echo, T2-weighted image is used to remove extracerebral components of the visual system. A three-dimensional (3D) connectivity analysis then identifies the largest connected volume as the brain. Five dual-echo fast spin-echo images acquired by repeated scanning of the same normal volunteer were used to verify reproducibility; and coronal and axial acquisitions from another normal volunteer to demonstrate the method's robustness to data collected with non-cubic voxels. Images acquired from five individuals with Alzheimer's disease are also presented to show that the algorithm can be used in cases of non-normative anatomy. Validity is affirmed by demonstrating that cerebral volumes estimated by this method for all 12 images are highly correlated (R = 0.98) with estimates obtained by an expert human operator.

Algorithms↗

PubMed1996

CONRAD: a method for identification of variable and conserved regions within proteins by scale-space filtering.

Advanced sequencing techniques allow rapid deduction of individual amino acid sequences of highly related proteins. Due to their quasi-species nature, viral genomes (e.g. HIV-1) represent one of the most common sources of related proteins. Another example of related proteins are immunoglobulins. Local differences in amino acid conservation are useful indicators of potential domain structures and immunological or functional epitopes prior to structural analysis of proteins. Although variability indices can be calculated by several methods, delineation of boundaries between sequence stretches with similar variability indices is left to the user. We use algorithmic scale-space filtering for delineation of conserved and variable sequence stretches within a protein which is performed on an algorithmic basis avoiding arbitrary assignments. Out method correctly identified variable regions for the human immunoglobulin lambda-chain V-regions (subgroup I). Prediction of the variable regions of the HIV-1 gp120 env protein was in agreement with empirical derived definitions. These examples indicate that our method is useful for the regional assignment of protein variability solely on the basis of amino acid sequences.

Algorithms↗

PubMed1991

Statistical analysis of collagen alignment in ligaments by scale-space analysis.

Injuries to ligaments in the knee are common in sports and other physical activities. Some clinical methods are available for qualitatively evaluating the degree of ligament injury and healing. It is, however, desirable to objectively assess the healing of ligaments and to predicate optimal treatment on quantitative measurements of their structure. Information such as areas of coverage and spatial orientations of collagen fibrils, for example, may provide important information about the internal structure of ligament tissues. Since normal ligament tissues are made up of collagen fibrils which are highly organized, they can be considered as oriented piecewise linear patterns. In this paper, we propose a computational technique for statistical analysis of collagen alignment in ligament images using the scale-space approach. In this method, a ligament image is preprocessed by a sequence of filters which are second derivatives of two-dimensional Gaussian functions with different scales. This gives a set of zero-crossing maps (the scale space) from which a stability map is generated. Significant linear patterns are captured by analyzing the stability map. The directional information in terms of orientation distributions of the collagen fibrils in the image and the area covered by the fibrils in specific directions are extracted for statistical analysis. Examples illustrating the performance of this method with scanning electron microscope images of the collagen fibrils in healing rabbit medial collateral ligaments are presented in this paper.

Animals↗

PubMed1998

Detection of the carpal bone contours from 3-D MR images of the wrist using a planar radial scale-space snake.

In this paper we consider the problems encountered when applying snake models to detect the contours of the carpal bones in 3-D MR images of the wrist. In order to improve the performance of the original snake model introduced by Kass [1], we propose a new image force based on one-dimensional (1-D) second-order Gaussian filtering and contrast equalization. The improved snake is less sensitive to model initialization and has no tendency to cut off contour sections of high curvature, because 1-D radial scale-space relaxation is used. Contour orientation is used to minimize the influence of neighboring image structures. Due to 1-D contrast equalization an intensity insensitive measure of external energy is obtained. As a consequence a good balance between internal and external energetic contributions of the snake is established, which also improves convergence. By incorporating this new image force into the snake model, we succeed in accurate contour detection, even when relatively high noise levels are present and when the contrast varies along the contours of the bones.

Algorithms↗

PubMed2000

Automatic recognition of biological shapes with and without representations of shape.

In this work it is described how to use the curvature function, the Fourier descriptors, and the coordinate functions of a contour to achieve automatic recognition of biological shapes. Those representations of shape and the coordinate functions were applied to recognize human corneal endothelial cells embedded in a sample of tissue. We assume that when the coordinates of the points of contours are analyzed directly, no representation of shape is being used. We applied scale-space filtering to the coordinate functions, to compensate the effects of scaling and to minimize the error due to quantization. A technique for compensating the effects of rotation, with or without the use of a representation of shape, is proposed. Our results show that, for a wide range of biological shapes, no representation of shape is required to solve or avoid the problems caused by translation, scaling, and rotation. We conclude that for certain applications the use of a representation of shape can provide some advantages. However, the coordinate functions of contours, evolved in scale-space, can be efficiently used, yielding even better results in applications of robotics and computer vision related to the recognition of biological shapes.

Cell Biology↗

PubMed1996

Is correspondence search in human stereo vision a coarse-to-fine process?

One possible strategy for the solution of the correspondence problem of stereo matching is the coarse-to-fine mechanism: The matching process starts with a lowpass-filtered version of the stereogram where only a few, high-contrast image features can be extracted and the probability of false matches is therefore low. In subsequent stages, information from higher spatial frequencies is used gradually to improve the correspondence data obtained on the coarser scales. Coarse-to-fine strategies predict that information from coarse scale is used to disambiguate matching information on finer scales. We have tested this prediction by means of the wallpaper illusion using periodic intensity-profiles with different matching ambiguities on different spatial scale. Our psychophysical experiments show (i) that unambiguous information at coarse scale is not always used to disambiguate finer scale information, (ii) that unambiguous fine-scale information can be used to disambiguate coarse-scale information and (iii) that low spatial frequency is more efficient for disambiguation than higher frequency. We conclude that the human stereo vision system does not always proceed from coarse to fine. As an alternative scheme for scale-space integration, we discuss more symmetric schemes such as maximum likelihood combinations of data from different channels.

Adult↗

PubMed1999

Interaction of ON and OFF pathways for visual contrast measurement.

We propose a novel model of visual contrast measurement based on segregated On and Off pathways. Two driving forces have shaped our investigation: (1) establishing a mechanism selective for sharp local transitions in the luminance distribution; (2) generating a robust scheme of oriented contrast detection. Our starting point was the architecture of early stages in the mammalian visual system. We show that the circuit behaves as a soft AND-gate and analyze the scale-space selectivity properties of the model in detail. The theoretical analysis is supplemented by computer simulations in which we selectively investigate key functionalities of the proposed contrast detection scheme. We demonstrate that the model is capable of successfully processing synthetic as well as natural images, thus illustrating the potential of the method for computer vision applications.

Computer Simulation↗

PubMed2001

Automatic recognition of biological shapes using the Hotelling transform.

We present two contour-based techniques, for computerized object recognition that avoid the difficulties due to translations, rotations and scaling. Our techniques do not require any shape representation. The first technique uses the scale-space filtered coordinate functions of contours and their "largest diameters". The second, uses the Hotelling transform of the vector representations of the points of contours. We applied both techniques to recognize human corneal endothelial cells, embedded in a sample of tissue. The results obtained using the Hotelling transform represent a considerable improvement when compared to those previously obtained using the coordinate functions, the curvature function and Fourier descriptors as representations of shape.

Artificial Intelligence↗

PubMed1999

Automatic segmentation of contours of corneal cells.

A fully automatic computerized method for segmenting contours of corneal endothelial cells is proposed. As part of the method, scale-space filtering (i.e. Gaussian filtering) is used to achieve tasks different from noise removal. This type of filtering is applied making use of the separability property of Gaussian kernels, avoiding the erosion of images. A variant of unsharp masking is used to considerably increase the visibility of dark areas of images. It is shown how the overflow that occurs when two images are subtracted can be handled to produce better results than normal unsharp masking. The method is exemplified with a low quality specular micrograph. To test the performance of the method, its output is used to automatically calculate the average cell size of images of different samples of tissue and different visual quality. The obtained results are successfully compared to those obtained with a manual semi-automatic method. A method for reading the segmented contours is suggested as well as two shape representations to achieve morphometric analysis of individual cells.

Algorithms↗

PubMed2000

Wavelet transforms in estimating scatterer spacing from ultrasound echoes.

Ultrasound echoes from organs such as the liver display resolvable periodicity due to regular scattering centers within tissue. The spacing among such scattering centers has been proposed as a signature to characterize diffuse and focal diseases of the liver. Even though it is highly desirable to be able to estimate an inter-scatterer-spacing (ISS) distribution, current methods can estimate only the mean value of scatterer spacing (MSS) over a tissue length. In this paper, we propose a wavelet transform-based technique that is capable of estimating the location of each scattering center, making it possible to obtain the ISS distribution. We represent liver tissue with a point scatterer model, and show, via computer simulations, that the use of multi-scale information in the wavelet scale-space allows us to estimate the locations of regular scattering centers. We show that both the observation noise and random ultrasound returns from unresolvable tissue microstructure can be removed successfully in the wavelet domain via the properties of the modulus maxima sequence of observation across different scales.

Computer Simulation↗

PubMed2002

Wavelet analysis of vortex tubes in experimental turbulence.

This paper proposes a method to study vortex tubes in one-dimensional velocity data of experimental turbulence. Vortex tubes are detected as local maxima on the scale-space plot of wavelet transforms of the velocity data. Then it is possible to extract a typical velocity pattern. The result for data obtained in a wind tunnel is consistent with those of three-dimensional direct numerical simulations.

Journal Article↗

PubMed2001

Algorithm for data clustering in pattern recognition problems based on quantum mechanics.

We propose a novel clustering method that is based on physical intuition derived from quantum mechanics. Starting with given data points, we construct a scale-space probability function. Viewing the latter as the lowest eigenstate of a Schrödinger equation, we use simple analytic operations to derive a potential function whose minima determine cluster centers. The method has one parameter, determining the scale over which cluster structures are searched. We demonstrate it on data analyzed in two dimensions (chosen from the eigenvectors of the correlation matrix). The method is applicable in higher dimensions by limiting the evaluation of the Schrödinger potential to the locations of data points.

Journal Article↗

PubMed2002

Parameter space warping: shape-based correspondence between morphologically different objects.

This paper presents a novel and comprehensive method for the automated determination of correspondences between two morphologically different two-dimensional (2-D) or three-dimensional (3-D) objects. Correspondences are determined by warping parametric representations of the objects to be matched. The warp is guided by the minimization of a similarity criterion function that measures features related to structural correspondence, including Euclidian point-to-point distance and differences in normals and curvature. The method uses a continuous harmonic parameterization for both the object and the warp, which provides: 1) a high degree of computational efficiency; 2) robust extraction of differential features, not subject to discretization errors or noise amplification in differentiation; 3) direct formulation of constraints to avoid overlaps in the resulting correspondence set; and 4) a scale-space paradigm of object shape and warp. The new method does not search for individual landmarks, but operates with a complete, integrated representation of the object geometry. The method was tested on 2-D and 3-D objects with substantial shape differences. Results demonstrated substantial improvements of 2%-33% in correspondence accuracy and 15%-59% in correspondence quality compared with direct registration methods.

Algorithms↗

PubMed2000

Multiresolution spot detection by means of entropy thresholding.

Many imaging applications deal with the detection of small targets or spots embedded within an inhomogeneous background. We present a method that accomplishes a multiresolution detection on the wavelet-transformed image. The targets are separated from the background by the exploitation of Renyi's information, which is evaluated at the different decomposition levels of the wavelet transform. The scale-dependent candidate detections are successively combined by means of majority voting for final detection. Connections with results provided in different fields such as multifractal analysis, generalized information measures in scale-space, and cross-entropy analysis in fine-to-coarse transformations are discussed. Detection performance is investigated through an example from medical image analysis.

Diagnostic Imaging↗