Showing papers on "Structuring element published in 2003"

Multiscale morphological segmentation of gray-scale images

Abstract: In this paper, the authors have proposed a method of segmenting gray level images using multiscale morphology. The approach resembles the watershed algorithm in the sense that the dark (respectively bright) features which are basically canyons (respectively mountains) on the surface topography of the gray level image are gradually filled (respectively clipped) using multiscale morphological closing (respectively opening) by reconstruction with isotropic structuring element. The algorithm detects valid segments at each scale using three criteria namely growing, merging and saturation. Segments extracted at various scales are integrated in the final result. The algorithm is composed of two passes preceded by a preprocessing step for simplifying small scale details of the image that might cause over-segmentation. In the first pass feature images at various scales are extracted and kept in respective level of morphological towers. In the second pass, potential features contributing to the formation of segments at various scales are detected. Finally the algorithm traces the contours of all such contributing features at various scales. The scheme after its implementation is executed on a set of test images (synthetic as well as real) and the results are compared with those of few other standard methods. A quantitative measure of performance is also formulated for comparing the methods.

Automatic Extraction of Filaments in Hα Solar Images

Abstract: This paper presents a new method which allows for the automatic extraction and tracking of the evolution of filaments in solar images. Series of Hα full-disk images are taken in regular time intervals to observe the changes of the solar disk features. In each picture, the solar chromosphere filaments are identified for further evolution examination. Two alternative preprocessing techniques converting grayscale images into black-and-white pictures with enhanced chromosphere granularity are examined: local thresholding based on median values and global thresholding with brightness and area normalization. The next step employs morphological closing operations with multi-directional linear structuring elements to extract elongated shapes in the image. After logical intersection of directional filtering results, remaining noise is removed from the final outcome using morphological dilation and erosion with a circular structuring element. Experimental results show that the developed technique can achieve excellent results in detecting large filaments and good detection rates for small filaments.

An improved morphological approach to background normalization of ECG signals

Abstract: This paper describes an improved morphological approach to remove baseline wander from neonatal electrocardiogram (ECG) signals, with particular emphasis on preserving the ST segment of the original signal. The algorithm consists of two stages of morphological processing. First, the QRS complex and impulsive noise component due to skeletal muscle contractions etc., are detected and removed from the input signal. Second, the corrected QT interval (QTc) and RR interval are used to determine a structuring element. With this structuring element, the same morphological operation as in the first stage is then applied to the QRS-removed signal to obtain and remove the baseline wander. The performance of the algorithm is evaluated with simulated and real ECGs. Compared with an existing morphological method, there is a substantial improvement, especially in reducing distortion of the baseline waveform within the PR and QT intervals.

Morphological transformations and feature extraction of urban data with high spectral and spatial resolution

Abstract: The classification of urban data with high spectral and spatial resolution is considered. For processing, a morphological profile is constructed. The morphological profile is based on the repeated use of opening and closings with a differently sized structuring element. Morphological profiles have been shown to contain redundancies. Therefore, feature extraction is applied on the profile. The morphological approach is applied in experiments on high resolution DAIS remote sensing data from an urban area. To apply the morphological approach on the DAIS data, the first principal component is used as a basis for the morphological transformations. In experiments, the use of the morphological method performs well in terms of classification accuracies. With feature extraction, it is observed that classification on reduced features gives higher accuracies than in the original feature space.

Face classification using curvature-based multi-scale morphology

Abstract: An image classification system uses curvature-based multi-scale morphology to classify an image by its most distinguishing features. The image is recorded in digital form. Curvature features associated with the image are determined. A structuring element is modulated based on the curvature features. The shape of the structuring element is controlled by making it a function of both the scaling factor and the principal curvatures of the intensity surface of the face image. The structuring element modulated with the curvature features is superimposed on the image to determine a feature vector of the image using mathematical morphology. When this Curvature-based Multi-scale Morphology (CMM) technique is applied to face images, a high-dimensional feature vector is obtained. The dimensionality of this feature vector is reduced by using the PCA technique, and the low-dimensional feature vectors are analyzed using an Enhanced FLD Model (EFM) for superior classification performance.

Design and implementation of a fuzzy hardware structure for morphological color image processing

Abstract: A hardware implementation of a fuzzy processor suitable for morphological color image processing applications is presented for the first time. From the hardware point of view, only a small number of algorithms for hardware implementation of soft gray-scale morphological filters have been reported in the literature, since research in mathematical morphology focuses mainly on possible extensions of the standard definitions (e.g., color and fuzzy mathematical morphology). The proposed digital hardware structure is based on a sequence of pipeline stages, and parallel processing is used in order to minimize computational times. It is capable of performing the basic morphological operations of standard and soft erosion/dilation for color images of 24-bit resolution. For the computation of morphological operations, a 3 /spl times/ 3-pixel image neighborhood and the corresponding structuring element are used. However, the system can be easily expanded to accommodate windows of larger sizes. The architecture of the processor is generic; the units that perform the fuzzy inference can be utilized for other fuzzy applications. It was designed, compiled, and simulated using the MAX+PLUS II Programmable Logic Development System by Altera Corporation. The fuzzy processor exhibits a level of inference performance of 601 KFLIPS with 54 rules, and can be used for real-time applications where the need for short processing times is of the utmost importance. The selection of a latest technology computer system (Pentium 4/3 GHz with SSE-2) can speed up image processing applications, but the time required still cannot be compared to the corresponding time using this hardware structure.

Estimation of the directional measure of planar random sets by digitization

Abstract: Estimation methods for the directional measure of a stationary planar random set Z, based only on discretized realizations of Z, are discussed. Properties of the discretized set that can be derived by comparing neighbouring grid points are used. Larger grid configurations of more than two grid points are considered. It is shown that the probabilities of observing the various types of configurations can be expressed in terms of the first contact distribution function of Z (with a finite structuring element). An important prerequisite result concerning deterministic dilation areas is also established. The inference on the mean normal measure based on 2 x 2 configurations is discussed in detail.

Morphological operations by locally variable structuring elements and their applications to region extraction in ultrasound images

Abstract: In this paper, we define a new adaptive morphological operation, in which the value of a structuring element varies adaptively depending on the local intensity information of the processing image of interest. We prove that the proposed adaptive morphological operation has almost the same mathematical structure and properties as the conventional ones have. There are many useful functions in the method. Among them are the opening and closing, which implement both smoothing of the image and emphasizing of the edges at a time. Conventional opening operation has a smoothing function but not both. Applying our method to relatively unclear images such as ultrasound ones with speckle noise, its usefulness can be found in extracting regions. We also discuss parameter setting, and its enhancement effect together with experimental results is demonstrated. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 34(3): 33–43, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10196

Discrete Morphology with Line Structuring Elements

Abstract: Discrete morphological operations with line segments are notoriously hard to implement. In this paper we study different possible implementations of the line structuring element, compare them, and examine their rotation and translation invariance in the continuous domain. That is, we are interested in obtaining a morphological operator that is invariant to rotations and translations of the image before sampling.

Primitive and point configuration texture model and primitive estimation using mathematical morphology

Abstract: A model for texture description, called "Primitive and Point Configuration (PPC) texture model," and an estimation method of the primitive, which is an elementary object for configuring a texture, are proposed in this paper. The PPC texture model regards that a texture is composed by arranging grains that are derived from one or a few primitives by some modification. The primitive shape is estimated by the principle that the primitive resembling the grains best should be the optimal estimation. This estimation is achieved by finding the structuring element that minimizes the integral of the size distribution function of a target texture.

On the Representation of Fuzzy Spatial Relations in Robot Maps

Abstract: Publisher Summary The chapter focuses on the fuzzy mathematical morphology that can be used to compute approximate spatial relations between objects in a robot map. Mathematical morphology is originally based on set theory. Introduced in 1964 by Matheron to study porous media, mathematical morphology that has rapidly evolved into a general theory of shape and its transformations, and it has found wide applications in image processing and pattern recognition. The four basic operations of mathematical morphology are dilation, erosion, opening, and closing. The key step is to represent the space in the robot's environment by an occupancy grid, and to treat this grid as a grey-scale image. This approach allows applying techniques from the field of image processing for extracting spatial information from grid. The chapter also discusses the use of this approach in the context to one particular type of robot maps, called topology-based maps that are built from occupancy grids. Fuzzy mathematical morphology can also be used to represent and compute in a uniform setting several types of relative position information, such as distance, adjacency and directional relative position.

Image analysis for the study of chromatin distribution in cell nuclei with application to cervical cancer screening

Abstract: This thesis describes a set of image analysis tools developed for the purpose of quantifying the distribution of chromatin in (light) microscope images of cell nuclei. The distribution or pattern of chromatin is influenced by both external and internal variations of the cell environment, including variations associated with the cell cycle, neoplasia, apoptosis, and malignancy associated changes (MACs). The quantitative characterisation of this pattern makes possible the prediction of the biological state of a cell, or the detection of subtle changes in a population of cells. This has important application to automated cancer screening. The majority of existing methods for quantifying chromatin distribution (texture) are based on the stochastic approach to defining texture. However, it is the premise of this thesis that the structural approach is more appropriate because pathologists use terms such as clumping, margination, granulation, condensation, and clearing to describe chromatin texture, and refer to the regions of condensed chromatin as granules, particles, and blobs. The key to the structural approach is the segmentation of the chromatin into its texture primitives. Unfortunately all of the chromatin segmentation algorithms published in the literature suffer from one or both of the following drawbacks: (i) a segmentation that is not consistent with a human's perception of blobs, particles, or granules; and (ii) the need to specify, a priori, one or more subjective operating parameters. The latter drawback limits the robustness of the algorithm to variations in illumination and staining quality. The structural model developed in this thesis is based on several novel low-, med-ium-, and high-level image analysis tools. These tools include: a class of non-linear self-dual filters, called folding induced self-dual filters, for filtering impulse noise; an algorithm, based on seeded region growing, for robustly segmenting chromatin; an improved seeded region growing algorithm that is independent of the order of pixel processing; a fast priority queue implementation suitable for implementing the watershed transform (special case of seeded region growing); the adjacency graph attribute co-occurrence matrix (AGACM) method for quantifying blob and mosaic patterns in the plane; a simple and fast algorithm for computing the exact Euclidean distance transform for the purpose of deriving contextual features (measurements) and constructing geometric adjacency graphs for disjoint connected components; a theoretical result establishing an equivalence between the distance transform of a binary image and the grey-scale erosion of its characteristic function by an elliptic poweroid structuring element; and a host of chromatin features that can be related to qualitative descriptions of chromatin distribution used by pathologists. In addition, this thesis demonstrates the application of this new structural model to automated cervical cancer screening. The results provide empirical evidence that it is possible to detect differences in the pattern of nuclear chromatin between samples of cells from a normal Papanicolaou-stained cervical smear and those from an abnormal smear. These differences are supportive of the existence of the MACs phenomenon. Moreover the results compare favourably with those reported in the literature for other stains developed specifically for automated cytometry. To the author's knowledge this is the first time, based on a sizable and uncontaminated data set, that MACs have been demonstrated in Papanicolaou stain. This is an important finding because the primary screening test for cervical cancer, the Papanicolaou test, is based on this stain.

A method for automatic infrared point target detection in a sea background based on morphology and wavelet transform

Abstract: A method is developed for the detection and segmentation of spot targets at sea surface. Firstly, the Sea-Sky-Division-Line (SSDL), close to the horizon, is detected by wavelet-transform to mark out the Target Recognition Region (TPR), which can reduce the target searching range. A Row average grayscale substraction (RAGS) operation is employed to correct the blur caused by the non-linearity distribution of the temperature field. To repress the clutter in the background and increase the SNR of the image, a morphology Top-Hat filter is utilized. Then, the image is opening by selecting a proper structuring element to acquire a few potential target points. Through searching the maximal intensity and determining a threshold, most of the false alarms can be eliminated and the doubtful targets can be segmented. When the SSDL is visible, the real point-target can be retained according to the TPR and the false target can be discarded. Under the conditions of invisibility of SSDL for it is outsdie of the image or it is obscure due to the weather, the segmented target is the real target. The experiment result shows that the method can effectively detect and segment infrared point target in complex sea background.

Convex structuring element decomposition for single scan binary mathematical morphology

Abstract: This paper presents a structuring element decomposition method and a corresponding morphological erosion algorithm able to compute the binary erosion of an image using a single regular pass whatever the size of the convex structuring element. Similarly to classical dilation-based methods [1], the proposed decomposition is iterative and builds a growing set of structuring elements. The novelty consists in using the set union instead of the Minkowski sum as the elementary structuring element construction operator. At each step of the construction, already-built elements can be joined together in any combination of translations and set unions. There is no restrictions on the shape of the structuring element that can be built. Arbitrary shape decompositions can be obtained with existing genetic algorithms [2] with an homogeneous construction method. This paper, however, addresses the problem of convex shape decomposition with a deterministic method.

Convex Structuring Element Decomposition for Single Scan Binary Mathematical Morphology

Abstract: This paper presents a structuring element decomposition method and a corresponding morphological erosion algorithm able to compute the binary erosion of an image using a single regular pass whatever the size of the convex structuring element.

Automatic target detection by optimal morphological filters

Abstract: It is widely accepted that the design of morphological filters, which are optimal in some sense, is a difficult task. In this paper a novel method for optimal learning of morphological filtering parameters (Genetic training algorithm for morphological filters, GTAMF) is presented. GTAMF adopts new crossover and mutation operators called the curved cylinder crossover and master-slave mutation to achieve optimal filtering parameters in a global searching. Experimental results show that this method is practical, easy to extend, and markedly improves the performances of morphological filters. The operation of a morphological filter can be divided into, two basic problems including morphological operation and structuring element (SE) selection: The rules for morphological operations are predefined so that the filter's properties depend merely on the selection of SE. By means of adaptive optimization training, structuring elements possess the shape and structural characteristics of image targets, and give specific information to SE. Morphological filters formed in this way become certainly intelligent and can provide good filtering results and robust adaptability to image targets with clutter background.

Basic morphological operations, band-limited images and sampling

Abstract: Morphological operations are simple mathematical constructs, which have led to effective solution for many problems in image processing and computer vision. These solutions employ discrete operators and are applied to digitized images. The mathematics behind the morphological operators also exists in the continuous domain, the domain where the images came from. We observed that the discrete operators cannot reproduce the results obtained by the continuous operators. The reason for this is that neither the operator (the structuring element) nor the result of the operation are band-limited, and thus cannot be represented by equidistant samples without loss of information. The differences between continuous-domain and discrete-domain morphology are best shown by the dependency of the discrete morphology on subpixel translations and rotations of the images before digitization. This article describes an algorithm that applies continuous-domain morphology to properly sampled images. We implemented the dilation for one-dimensional images (signals), and with it constructed the erosion, the closing and the opening. We provide a discussion on a possible extension to higher-dimensional images.

Morphological neural networks for automatic target detection by simulated annealing learning algorithm

Abstract: A practical neural network model for morphological filtering and a simulated annealing optimal algorithm for the network parameters training are proposed in this paper. It is pointed out that the optimal designing process of the morphological filtering network in fact is the optimal learning process of adjusting network parameters (structuring element, or SE for short) to accommodate image environment. Then the network structure may possess the characteristics of image targets, and so give specific information to the SE. Morphological filters formed in this way become certainly intelligent and can provide good filtering results and robust adaptability to complex changing image. For application to motional image target detection, dynamic training algorithm is applied to the designing process using asymptotic shrinking error and appropriate network weights adjusting. Experimental results show that the algorithm has invariant property with respect to shift, scale and rotation of moving target in continuing detection of moving targets.

X-ray bone fracture subtraction using geodesic active contour and mathematical morphology operations

Abstract: This paper presents a new approach on deformable models that combines geodesic active contour model with mathematical morphology operations and minimum path-finding algorithms. Morphology operations are applied to define the constraints of the geodesic active contour model. The size and shape of the structuring element for morphology operations describe a local window that collect and filter the local image gradient information. This local information is fed to the global constraints of the energy function of the geodesic active contour. The focus of this research is to investigate the optimal constraints that allow the model to be able to perform the segmentation task of the bone fracture subtraction of X-ray image of human arms over a noisy cast background. The model developed in this project speeded up the method of geodesic active contour, improved the result of the edge finding by minimizing the energy function based on a local window and avoided the curve attracted to the small gaps on the weak edges to improve the accuracy of segmentation.

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.

Automatic target recognition in infrared image using morphological genetic filtering algorithm

Abstract: A novel method for optimal morphological filtering parameters, namely the genetic training algorithm for morphological filters (GTAMF) is presented in this paper. GTAMF adopts new crossover and mutation operators called the curved cylinder crossover and master-slave mutation, to achieve optimal filtering parameters in a global searching. Experimental results show that this method is practical, easy to extend, and improves the performances of morphological filters. The operation of a morphological filter can be divided into two basic problems that include morphological operation and structuring element (SE) selection. The rules for morphological operations are predefined so the filter's properties depend merely on the selection of SE. By means of adaptive optimizing training, structuring elements possess the shape and structural characteristics of image targets, namely some information can be obtained by SE. Morphological filters formed in this way become certainly intelligent and can provide good filtering results and robust adaptability to image targets with clutter background.

Morphological pre-processing for classification of hyperspectral data from urban areas

Abstract: Classification of hyperspectral data with high spatial resolution is discussed. A method based on mathematical morphology for pre-processing of the hyperspectral data is investigated. In this approach, opening and closing morphological transforms are used in order to isolate bright (opening) and dark (closing) structures in images, where bright/dark means brighter/darker than the surrounding features in the images. Then, a morphological profile is constructed based on the repeated use of openings and closings with a differently sized structuring element. In order to apply the morphological approach to hyperspectral data, principal components are computed. Then, the principal components are used as base images for the morphological profiles. The use of extended morphological profiles, based on more than one principal component is proposed. In experiments, two data sets are classified. The proposed method performs well in terms of classification accuracies. It gives similar overall accuracies to statistical approaches.

Binary Opening and Closing

Abstract: Besides the two primary operations of erosion and dilation, there are two secondary operations that play key roles in morphological image processing, these being opening and its dual, closing. We focus mostly on opening, the properties of closing usually being analogous via complementation. Although opening is defined in terms of erosion and dilation, it possesses a more geometric formulation in terms of structuring element fits that is the basis for its application.

Design of optimal in morphological filters based on neural nets

Abstract: The paper presents a novel algorithm to design optimal parameters of a gray morphological filter with neural network. Two neural network structures of dilation operation and erosion operation are giren. Among them, weights of neural network are represented by a structuring element and trained by a learning algorithm based on d-learning criterion. The results of numerical examples will be shown that the algorithm has better filter properties than the conventional morphological ones.

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 R A (n) indexed by an integer n, followed by downsampling. For n = 0, R A (n) equals the erosion e A with structuring element A, whereas for n > 0, R A (n) equals the erosion e A followed by n conditional dilations, which for n→ oo 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.

Processing with nonlinear filters satellite imagery

Abstract: This paper describes the aim towards a nonlinear processing system for satellite image enhancement and smoothing that prepares image for a successful feature extraction through edge detection. Emphasis was given to coastlines, man made objects such as airports, dams, buildings and linear features such as roads and parcel boundaries. Rank order morphological operators and adaptive filtering were employed leading to a promising result. Adaptive filtering was adopted to smooth the image, homogenize regions and at the same time prohibit edge blurring, since high frequency areas in the image are protected. Morphological operators based on rank filters were also implemented because they are often more robust to noise and shape variations than morphological operators with plain structuring element. A survey concerning the shape and the size for the structuring element used for the morphological operators is presented. Structuring element’s shape can lead to certain transformation of desired features geometry and size controls the scale space, trying to retain only features at certain desirable scales. The nonlinear processing system was applied to SPOT HRV (10-meters ground resolution) and IKONOS PAN (1-meter ground resolution) satellite imagery and is demonstrated with examples.

Morphological-transformation-based technique of edge detection and skeletonization of an image using a single spatial light modulator

Abstract: A technique of optically detecting the edge and skeleton of an image by defining shift operations for morphological transformation is described. A (2 × 2) source array, which acts as the structuring element of morphological operations, casts four angularly shifted optical projections of the input image. The resulting dilated image, when superimposed with the complementary input image, produces the edge image. For skeletonization, the source array casts four partially overlapped output images of the inverted input image, which is negated, and the resultant image is recorded in a CCD camera. This overlapped eroded image is again eroded and then dilated, producing an opened image. The difference between the eroded and opened image is then computed, resulting in a thinner image. This procedure of obtaining a thinned image is iterated until the difference image becomes zero, maintaining the connectivity conditions. The technique has been optically implemented using a single spatial modulator and has the advantage of single-instruction parallel processing of the image. The techniques have been tested both for binary and grey images.