Showing papers on "Structuring element published in 2002"

Application of Mathematical Morphology in Image Processing

Abstract: Mainly introduced the mathematical morphological operators and their application in image processing In addition, it introduced the algorithm of structuring element selection

Detection and analysis of lesions in contact with a structural boundary

Abstract: An algorithm is disclosed that detects and analyzes possible lesions that are left in contact with a structural boundary. A morphological closing with a structuring element is performed along the boundary to detect lesions within the threshold of the structuring element. A deformable surface-based analysis is performed on distinctive surfaces of the structure for the identification of lesions. The integrated use of a deformable surface model and chamfer distance potential enables explicit representation of regularized, or smoothed, surfaces from which lesion candidates may be detected.

Feature-based detection of landmines in infrared images

Abstract: High detection performance is required for an operational system for the detection of landmines. Humanitarian de-mining scenarios, combined with inherent difficulties of detecting landmines on an operational (vibration, motion, atmosphere) as well as a scenario level (clutter, soil type, terrain), result in high levels of false alarms for most sensors. To distinguish a landmine from background clutter one or more discriminating object features have to be found. The research described here focuses on finding and evaluating one or more features to distinguish disk-shaped landmines from background clutter in infrared images. These images were taken under controlled conditions, with homogenous soil types. Two methods are considered to acquire shape-based features in the infrared imagery. The first method uses a variation of the Hough transformation to find circular shaped objects. The second method uses the tophat filter with a disk-shaped structuring element. Furthermore, Mahalanobis and Fisher based classifiers are used to combine these features.

Algorithms for openings of binary and label images with rectangular structuring elements

Abstract: Two new families of algorithms for computing openings of binary and label images are presented in this paper. The first family of algorithms is based on an horizontal scan, and a vertical scan that takes the result of the horizontal scan as input. With the results of these two scans it is possible to compute an opening with respect to a rectangle of any size. The second family of algorithms is based on the construction of an exhaustive list of rectangles included in an object. Rectangles of this list all have a maximal extension, i.e. no larger rectangle included in a region contains them. The opening then results from filling the output image with rectangles larger than the structuring element. After a description of the algorithms we provide a comparison of several algorithms in terms of computation time efficiency. The comparison shows that some of the new algorithms advantageously compete with existing algorithms.

A note on Park and Chin's algorithm [structuring element decomposition]

Abstract: A finite subset of Z/sup 2/ is called a structuring element. A decomposition of a structuring element A is a sequence of subsets of the elementary square (i.e., the 3/spl times/3 square centered at the origin) such that the Minkowski addition of them is equal to A. H. Park and R.T. Chin (see ibid., vol.17, no.1, p.2-15, 1995) developed an algorithm for finding the optimal decomposition of simply connected structuring elements (i.e., 8-connected structuring elements that contain no holes), imposing the restriction that all subsets in this decomposition are also simply connected. The authors show that there exist infinite families of simply connected structuring elements that have decompositions but are not decomposable according to Park and Chin's definition.

Grayscale image de-speckle algorithm

Abstract: An annular window-shaped structuring element is provided for image processing to remove speckles from a scanned image. The window-shaped structuring element is composed of two differently sized squares sharing the same geometric center-point. The pixel to be analyzed with the structuring element is at the center-point. The structuring element is used in a method to remove speckles from binary, grayscale, and/or color images by first eroding the image, detecting speckles relative to other pixels in the image, and removing declared speckles. The method may additionally include a halftoning module to protect halftone images.

Implementation efficiency of binary morphology

Abstract: The efficienc y of implementations of binary morphology is investigated, using both full image rasterops and word accumulation methods. All processing speeds are expressed in a way that is relatively independent of CPU speed and the sizes of both image and structuring element; namely, elementary pixel operations per CPU cycle (EPO/cyc). Options for handling boundary pixels are discussed. It is shown that use of successive full image rasterops is much slower than methods where the full structuring element is applied repeatedly to small parts of the image. Processing speeds of the former range from about 1 to 3 EPO/cyc, whereas the latter are typically between 4 and 7 times faster and range from 3 to 12 EPO/cyc. For small images using rasterops, vertical operations are about twice as fast as horizontal (3.2 vs 1.6 EPO/cyc); using word accumulation, vertical operations are only slightly faster than horizontal (12 vs 10 EPO/cyc). Performance on large images is reduced by a factor of between 2 and 4, due to slow reads and writes to main memory.

Classifying Fleece Fabric Appearance by Extended Morphological Fractal Analysis

Abstract: Image analysis techniques have been widely accepted as objective methods for evalu ating fabric appearance. This paper presents the development of a fairly new fractal analysis method (extended morphological fractal analysis) for characterizing polar fleece fabric appearance after abrasion. The digital gray level image is treated as a three- dimensional surface whose fractal dimension is calculated by performing a series of dilations and erosions on this surface and plotting the area of the resulting set of surfaces against the size of the structuring element. In contrast to a single morphological fractal .parameter, which is scale-invariant, extended fractal analysis is able to characterize fabric textures where the roughness of these textures is not necessarily scale-invariant. This approach can be used to physically describe surface roughness and texture regularity with the parameter MFV (multiscale fractal vector) and to classify the appearance grade with the Bayes classification method. Our experimen...

New morphological detection algorithm based on the hit-miss transform

Abstract: A new hit-miss transform (HMT) algorithm is proposed to de- tect distorted multiple objects in clutter. The HMT in morphology is used to locate a specific object in an input image. But the standard algorithm of the morphological HMT may cause problems in detecting various true- class objects. To provide efficient recognition of various true-class ob- jects in the input image, a new optical hit-miss morphological transform using the synthetic structuring element (SE) is proposed. The synthetic hit SE is composed of the intersection of all true-class hit SEs, and the synthetic miss SE is composed of the complement of the union of all true-class hit SEs. Simulation results show that the proposed algorithm can be used for the recognition of various true-class objects in an un- known input scene with only one HMT operation. © 2002 Society of Photo-

Segmentation of spinal cord images by means of watershed and region merging together with inhomogeneity correction

Abstract: The paper presents a morphological method for segmentation of high field Magnetic Resonance (MR) images of the human spinal cord and extraction of the gray matter mask. These images are of low quality and poor contrast. The inhomogeneity of brightness in the image is usually more pronounced than the difference in brightness between the gray matter and the white matter. Due to this inhomogeneity, it is very hard to use watershed segmentation for automatic extraction of the gray matter, and what remains is manual pointing out of a hundred or more regions belonging to the gray matter. However, as shown in the paper, by using the White Top Hat (WTH) transform with a large structuring element, one can correct the images, significantly reducing the inhomogeneity and appropriately modifying individual region statistics. In particular, watershed segmentation is carried out on the original image, whereas region statistics used for region merging are calculated from the corrected image. Then the extraction of the gray matter mask is carried out in a semi-automatic way, with the user pointing out the first region belonging to the gray matter area, and the program selecting subsequent neighboring regions based on the statistics of the regions. The method was tested on images coming from different cross-sections of the spinal cord, and the results indicate that the process of extracting the gray matter mask has been significantly speeded up and improved.

Mathematical morphology and spatial relationships: quantitative, semi-quantitative and symbolic settings

Abstract: Basic mathematical morphology operations rely mainly on local information, based on the concept of structuring element. But mathematical morphology also deals with more global and structural information since several spatial relationships can be expressed in terms of morphological operations (mainly dilations). The aim of this paper is to show that this framework allows to represent in a unified way spatial relationships in various settings: a purely quantitative one if objects are precisely defined, a semi-quantitative one if objects are imprecise and represented as spatial fuzzy sets, and a qualitative one, for reasoning in a logical framework about space. This is made possible thanks to the strong algebraic structure of mathematical morphology, that finds equivalents in set theoretical terms, fuzzy operations and logical expressions.

Regulated morphological operations with weighted structuring element

Abstract: In this paper we extend regulated morphological operations by using a weighted structuring element The proposed operations have more controllable parameters (a weight set), and so they display better performance than regulated morphological operations with no weighted structuring element in some cases An example confirms the proposed approach

Automatic text extraction in news images using morphology

Abstract: In this paper we present a new method to extract both superimposed and embedded graphical texts in a freeze-frame of news video. The algorithm is summarized in the following three steps. For the first step, we convert a color image into a gray-level image and apply contrast stretching to enhance the contrast of the input image. Then, a modified local adaptive thresholding is applied to the contrast-stretched image. The second step is divided into three processes: eliminating text-like components by applying erosion, dilation, and (OpenClose + CloseOpen)/2 morphological operations, maintaining text components using (OpenClose + CloseOpen)/2 operation with a new Geo-correction method, and subtracting two result images for eliminating false-positive components further. In the third filtering step, the characteristics of each component such as the ratio of the number of pixels in each candidate component to the number of its boundary pixels and the ratio of the minor to the major axis of each bounding box are used. Acceptable results have been obtained using the proposed method on 300 news images with a recognition rate of 93.6%. Also, our method indicates a good performance on all the various kinds of images by adjusting the size of the structuring element.

Separation of spiky transients in eeg/meg using morphological filters in multi-resolution analysis

Abstract: Epileptic electroencephalographic (EEG) data often contains a large number of sharp spiky transient patterns which are diagnostically important. Background activity is the EEG activity representing the normal pattern from the brain. Transient activity manifests itself as any non-structured sharp wave with dynamically short appearance as distinguished from the background EEG. Generally speaking, the amplitude change of background activity varies slowly with time and spiky transient activity varies quickly with pointed peaks.In this thesis, a method has been developed to automatically extract transient patterns based on morphological filtering in multiresolution representation. Using a simple structuring element (SE) to match a signal's geometrical shape, mathematical morphology is applied to detect the differences of morphological characteristics of signals. If a signal contains features consistent with the geometrical feature of the structuring element, a morphological filter can recognize and extract the signal of interest. The multiresolution scheme can be based on the wavelet packet transform which decomposes a signal into scaling and wavelet coefficients of different resolutions. The morphological separation filter is applied to these coefficients to produce two subsets of coefficients for each coefficient sequence: one representing the background activity and the other representing the transients. These subsets of coefficients are processed by the inverse wavelet transform to obtain the transient component and the background component. Alternatively, a morphological lifting scheme has been proposed for separation these two components. Experimental results on both synthetic data and real EEG data have shown that the developed methods are highly effective in automatic extraction of spiky transients in the epileptic EEG data.The interictal spike trains thus extracted from multiple electrode recordings are further analyzed. Their cross-correlograms are examined according to the stochastic point process model. Our experiment result has been verified by human experts' estimation.

Morphological texture analysis using optimization of structuring elements

Abstract: This paper proposes a method of texture analysis using morphological size distribution. Our framework is based on the concept that a texture is described by estimation of primitive, size distribution of grains derived from the primitive, and spatial distribution of the grains. We concentrate on estimation of primitive using an assumption on grain size distribution. We assume a model that grains are derived from one primitive, and a uniform size distribution since we consider target textures containing grains of various sizes. Thus the structuring element used for the measurement of size distribution is optimized to obtain the most uniform size density function. The optimized structuring element is an estimate of the primitive under the assumption. Simulated annealing algorithm is employed for the optimization.

A new class of morphological pyramids for multiresolution image analysis

Abstract: We study nonlinear multiresolution signal decomposition based on morphological pyramids. Motivated by a problem arising in multiresolution volume visualization, we introduce a new class of morphological pyramids. In this class the pyramidal synthesis operator always has the same form, i.e. a dilation by a structuring element A, preceded by upsampling, while the pyramidal analysis operator is a certain operator RA(n) indexed by an integer n, followed by downsampling. For n = 0, RA(n) equals the erosion ɛA with structuring element A, whereas for n > 0, RA(n) equals the erosion ɛA followed by n conditional dilations, which for n → ∞ is the opening by reconstruction. The resulting pair of analysis and synthesis operators is shown to satisfy the pyramid condition for all n. The corresponding pyramids for n = 0 and n = 1 are known as the adjunction pyramid and Sun-Maragos Pyramid, respectively. Experiments are performed to study the approximation quality of the pyramids as a function of the number of iterations n of the conditional dilation operator.

Decomposition of grey-scale morphological structuring elements in hardware

Abstract: Morphological image processing machines are not capable of handling large-size structuring elements. A new architecture for fast execution of the erosion/dilation operations in an up to 9 × 9-pixel, arbitrarily shaped, image window through decomposition of grey-scale morphological structuring element into 3× 3-pixel sub-domains is presented in this paper. The proposed hardware structure has been also implemented in VLSI and its throughput rate is 10 Mbytes/sec.

Object extraction using genetic algorithms and morphological processing

Abstract: This paper introduces a novel methodology for object detection using genetic algorithms and morphological processing. The method employs a kind of object oriented structuring element, which are derived using genetic algorithm operating. The population of morphological filters iteratively evaluated according to a statistical performance index corresponding to object extraction ability, and evolves into an optimal structuring elements using the evolution principles of genetic search. Experimental results of object extraction in high resolution satellite images are presented to illustrate the merit and feasibility of the proposed method.

Using mathematical morphology for correction of magnetic resonance images

Abstract: SUMMARY The paper presents a morphological method for brightness correction of Magnetic Resonance (MR) images, which makes possible the use of watershed technique for image segmentation and extraction of objects. As an example of image correction, extraction of the mask of the gray matter from the image of the human spinal cord is given. The described image correction is based on the use of the White Top Hat (WTH) transform with a large structuring element. As a result of the correction, brightness in the image becomes more homogeneous, and one can merge the watershed regions included in the mask of the gray matter in a semi-automatic procedure. This is a significant improvement in comparison with uncorrected images, for which development of such a procedure proved to be very difficult. The current paper is a simplified and abbreviated version of [10].

Statistical Property for Processing Gain of Lofargram Smoothing using Morphological Filter

Abstract: The methods for analyzing the temporal structures of passive sonar signals employ spectral analyses of the received signals. One of these methods is called the lofargram (low-frequency analysis record-gram). Fine-quality lofargrams are generally obtained by using averaging procedure, which constitutes a data smoothing method. However, the procedure causes the impulse noise to spread across the lofargram's time-axis. This makes it difficult to distinguish a target signal from a received signal on the lofargram. Morphological filtering is a nonlinear signal transformation that locally modifies the geometric features of signals. Morphological opening filter can be used effectively to suppress positive impulse noise in signals and images. The authors have proposed a method for smoothing a lofargram using morphological opening filter instead of averaging procedure. The effective improvements in processing gain and time resolution obtained in the experiment are compared with the results obtained using averaging procedure. This paper describes some statistical properties of the processing gain of a lofargram obtained using morphological opening filter. The processing gain is defined in the case of the line structuring element, and its theoretical equation is derived when the source signal is Gaussian noise. The results of the computer simulation agree approximately with the theoretical value obtained by this equation.

Morphological boundary detecting method in cineangiogram image

Abstract: Atherosclerotic coronary heart disease (CHD) is one of the commonest diseases that is heavily hazardous to people's health. Wall motion abnormalities of L.V. due to myocardia ischeamia caused by coronary atherosclerosis is a significant feature of CHD. This paper was designed to build up a foundation for automatic detection of L.V. contours according to the features of L.V. cineangiograms, for a further study of L.V. wall motion abnormalities. An algorithm that based on morphology for L.V. contours extracting was developed in this paper. As we know morphology is a kind of technique based upon set theory and it can be used for binary image and gray image processing. The principle and the geometrical meaning of morphological boundary detecting for image were discussed in this paper, and the selection of structuring element was analyzed. Comparison was made between morphological boundary detecting and traditional boundary detecting method, conclusion that morphological boundary detecting method has better compatibility and anti-interference capability was reached.