Showing papers on "Structuring element published in 1998"

Decomposition of arbitrarily shaped binary morphological structuring elements using genetic algorithms

Abstract: A number of different algorithms have been described in the literature for the decomposition of both convex binary morphological structuring elements and a specific subset of nonconvex ones. Nevertheless, up to now no deterministic solutions have been found to the problem of decomposing arbitrarily shaped structuring elements. This work presents a new stochastic approach based on genetic algorithms, in which no constraints are imposed on the shape of the initial structuring element nor assumptions are made on the elementary factors, which are selected within a given set.

Weighted morphological filter

Abstract: A novel morphological filter using weighted morphological operators is presented. The newly introduced operators employ a weighted structuring element and apply multiplication or division in place of addition and subtraction in classical morphological operations. Experimental results prove that the new operators' performance dominates those of classical operators for signals/images buried in salt & pepper, speckle and Gaussian noise.

Enhanced morphological reconstruction of SPM images

Abstract: In this paper we develop a new form of filter, designed to eliminate downward noise spikes from SPM images before they are reconstructed to remove tip shape features It is shown that a flat surface function is the optimum structuring element for the removal of downward noise and that this should have a dimension equal to or less than the radius of curvature of the probing tip By using the operation `closing' the original SPM image is operated on with such a function The holes present in the image which are smaller than the apex of the tip, as given by the downward noise of images, are eliminated This operation is called a `closing filter' Consequently, this filter is able to enhance the existing standard method, morphological reconstruction, when generating reconstructed images In comparison, the performance of this filter was found to be significantly better than that of the median filter Furthermore, the image artefact due to the tip-surface geometrical interaction was simulated and quantified

A fast algorithm for morphological operations with flat structuring element

Abstract: Flat structuring elements are commonly used in morphological operations. In this paper, a fast algorithm employing the result of previous searching area, which is determined by a domain-selection method, is proposed. It is applicable to structuring elements conforming to a constraint that its one-dimensional (1-D) Euler-Poincare constants, N/sup (1/)(x) and N/sup (1/)(y), at any x- or y-coordinate must be equal to 1. The proposed algorithm is compared with three other methods, namely threshold linear convolution of Kisacanin and Schonfeld (KS), structuring element decomposition of Shih and Mitchell (SM), and fast implementation of Wang and He (WH), in terms of the theoretical expected number of comparisons and experimental computation time. It is found that the proposed algorithm requires less computation time than KS and SM methods for nearly all sizes of square, octagon, and rhombus structuring elements, except for the size of 3/spl times/3. In addition, it is also more time efficient than the WH method, except for the square structuring element.

Segmentation of mammograms into distinct morphological texture regions

Abstract: Presents a comprehensive discussion on the segmentation of mammograms using morphological texture features. These features are derived from morphological granulometries with various structuring elements. Each structuring element captures a specific texture content. The segmentation is carried out in an unsupervised manner by applying the KL (Karhunen-Loeve) transform feature reduction and Voronoi clustering on the extracted morphological texture features. The evaluation of the segmentation outcome by a trained radiologist is provided.

Average Grain Size Determination using Mathematical Morphology and Texture Analysis

Abstract: Many industrial processes need information about material grain size. In this work we examined rolled chrome concentrate to determine the average grain size. Test material was sieved into 15 fractions, from 37 pm to 500 pm. The analysis method can be divided in three sections: preprocessing, feature extraction and classification. Mathematical morphology was used as preprocessing method, with gray-scale erosion and opening as operations. Feature extraction was implemented with first and second-order statistics. Finally, classification was performed with k-NN and minimum distance classifiers using leave-out method. We conclude that mathematical morphology with texture analysis can be used to determine average grain size of material. It is computationally easy and fast although less accurate to smaller grain classes. This is due to imaging errors and noise but also the fact that the ratio grain size versus size of structuring element must be large enough. Both opening and erosion operations can be used. Erosion is two times faster than opening to perform. Also the number of preprocessing operations can be, for example, reduced to three without the classification result will have a remarkable change.

Pattern recognition in automotive plates

Abstract: Security measures require to know which vehicles are either leaving or entering premises. To this end we have developed an algorithm which allows us to identify license plates in vehicles and compare them with a database. To this purpose we have used morphological signal processing. Morphological image processing is a technique that is becoming increasingly important for a wide range of image processing tasks. These tasks are performed by successive application of Minkowsky's primitive operations. The success or failure of this type of image processing is critically dependent on the efficiency with which these primitives are computed, the transformation type, and the structuring element used. In this paper we show an algorithm for pattern recognition using the minimal distance between shape spectrums (PECSTRUM), obtained from successive openings of binary images, with base on a structuring element invariant in rotation and translation.

Logical structural filters

Abstract: Binary granulometric filters are formed from unions of parameterized openings, a point passing the filter if and only if a translate of at least one structuring element fits in the image and contains the point. A granulometry induces a reconstructive granulometry by passing any image component not eliminated by the granulometry. As historically studied in the context of Matheron's granulometric theory, reconstructive granulometries appear as unions of reconstructive parameterized openings. The theory is extended to a much wider class of filters: a logical structural filter (LSF) is formed as a union of intersections of both reconstructive and complementary reconstructive openings. A reconstructive opening passes a component if and only if at least one translate of the structuring element fits inside; a complementary reconstructive opening passes a component if and only if no translate of the structuring element fits inside. The original reconstructive granulometries form the special class of disjunctive LSFs. Complement-free LSFsform granulometries in a slightly more general sense; LSFs containing complements are not increasing and therefore not openings. Along with the relevant algebraic representations for LSFs, the theories of optimal and adaptive granulometric filters are extended to LSFs, a systematic formulation of adaptive transitions is given, transition probabilities for adaptation are found, and two applications to biological imaging are presented.

Model-based shape recognition using recursive mathematical morphology

Abstract: This paper introduces a size invariant method to recognize two-dimensional binary shapes using the recursive erosion transform. Using recursive morphology with multiple structuring elements, the method takes constant time per pixel regardless of the scale of the shape model, and also works on noisy images without requiring noise removal. Results from experiments on 100 noisy images show the methodology is able to detect every shape model's scale and position with 13 false alarms and five misdetections out of 254 total translated and scaled models.

Morphological ordered gray-scale erosion for optical image processing

Abstract: An ordered gray-scale erosion is suggested according to the definition of hit-miss transform. Instead of using three operations, two images, and two structuring elements, the developed operation requires only one operation and one structuring element, but with three gray-scale levels. Therefore, a union of the ordered gray-scale erosions with different structuring elements can constitute a simple image algebra to program any combined image processing function. An optical parallel ordered gray-scale erosion processor is developed based on the incoherent correlation in a single channel. Experimental results are also given for an edge detection and a pattern recognition. (C) 1998 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(98)00306-7].

Morphological technique for clutter suppression in ATR

Abstract: This paper presents a morphological filtering technique that can be used for clutter suppression or target feature extraction. By using a special ring-shaped structuring element, the proposed morphological filter can either preserve or eliminate objects in sensor imagery based on size and shape characteristics. The ring-shaped structuring element makes the filter invariant to target rotation. Thus, detection performance is substantially improved for cases where the target width information is either inaccurate or insufficient for successful differentiation between targets and image clutter.

Approaches to decompose flat structuring element for fast overlapping search morphological algorithm

Abstract: The fast morphological algorithm, overlapping search (OS) algorithm, recently proposed by Lam and Li (1998), can only be applied to a flat structuring element (FSE) whose 1D Euler-Poincare constants, N/sup (1)/(x) and N/sup (1)/(y), at any x or y coordinate are equal to 1. Thus, an arbitrarily shaped structuring element must be decomposed to a set of constrained components before employing the fast algorithm. In the paper, a decomposition algorithm, based on line-scanning process, and an improved algorithm, based on conditionally maximal convex polygon (CMCP), are proposed to generalize the use of the overlapping search morphological algorithm for any arbitrary flat structuring element.

Automatic Detection of Clustered Microcalcifications Using Morphological Reconstruction

Abstract: This paper presents a method for automatic detection of clusters of microcalcifications (MCCs) in mammograms. As MCCs are small regions made up by groups of pixels brighter than their neighbours, in order to detect them the operators have to be based on the size and contrast. In mammography linear operators have been used like Laplacian-of-a-Gaussian (LoG), among others, approximated for the discrete integer approximation with the Difference-of-Gaussian (DoG) filter, as proposed in [1]. In this paper we explore operators based on non-linear theory, specifically based on mathematical morphology. This discipline has already been used in previous work [2] by means of the watershed, but in that paper the process needs user-introduced markers to reduce the oversegmentation produced by the watershed, so it is not a fully automatic detection method. In a previous work [3] we proposed to use the residue of the supremum of a number of openings each of them with a linear structuring element jointly with the Markov random field model (MRF) proposed in [4], which can eliminate the major part of the connective tissue reducing the false positive rate. In some cases this morphological operator is not enough. In this paper we studied the contribution of morphological reconstruction to improve the elimination of the connective tissue and so to reduce the false positive rate.

Multi FPGA processor for gray-scale morphology

Abstract: Local operations in image processing are often used, namely during the preprocessing step. On one hand, their implementation is expensive, on the other hand, they become efficient when they use a wide area neigbourhood. In this paper we propose a general method for synthesis of linear and non linear filters (mathematical morphology operators). The main interest of this method is that it can be applied to various types of filters: separable or not, factorizable or not and for any size of the filter kernel (convolution) or the structuring element (mathematical morphology). Prom this method, one can get an architecture in a straightforward way. We propose a re-configurable architecture, based upon reprogrammable circuits (FPGA: Field Programmable Gate Array). This multi FPGA architecture allows a real time implementation of any r × c size kernel (processing at video rate).

Adaptive feature enhancement of retinal vascular images using wavelet-based multiresolution analysis

Abstract: The goal of this research is to put forward a high efficient method that can generate clinically useful images with improved visualization of retinal vascular image features. Methods: (1) Multiresolution decomposition of an original retinal image into subband images via 2D wavelet transformation (WT). (2) At S equals 21, the LLMMSE estimate algorithm that is applicable for nonstationary image model is used in wavelet domain to reduce noise. At s equals 22, soft thresholding wavelet shrinkage technique is used in wavelet domain to reduce noise. At s >= 23, semisoft wavelet shrinkage technique is applied in wavelet domain to further reduce noise. (3) Opening of a gray-scale Adaf subband image by a gray-scale circular structuring element can generate a background image. Enhancement is achieved by using this opened image as a unsharp mask and then applying morphological spatial filtering technique to enhance vessel contrast. (4) Reconstruction of retinal image from modified subband images via inverse 2-DWT. Experimental results show that the proposed approach is able to adaptively enhance subtle vascular features, suppress noise and improve global visualization of retinal vascular images.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Computation of morphological texture features for medical imaging applications

Abstract: Texture is an important attribute which is widely used in various image analysis applications. Among texture features, morphological texture features are least utilized in medical image analysis. From a computational standpoint, extracting morphological texture features from an image is a challenging task. The computational problem is made even greater in medical imaging applications where large images such as mammograms are to be analyzed. This paper discusses an efficient method to compute morphological texture features for any geometry of a structuring element corresponding to a texture type. A benchmarking of the code on three machines (Sun SPARC 20, Pentium II based Dell 400 workstation, and SGI Power Challenge 10000XL) as well as a parallel processing implementation was performed to obtain an optimum processing configuration. A sample processed mammogram is shown to illustrate the code outcome.

Soft morphological structuring element decomposition

Abstract: In this paper a decomposition technique suitable for any grey-scale soft morphological structuring element is presented. A hardware structure implementing this technique is also presented.

Queueing interpretation of adaptive reconstructive multiparameter τ-opening filters

Abstract: A multiparameter binary (tau) -opening is a union of parameterized openings in which parameters for each opening are individually defined and a structuring element can be parameterized relative to both size and shape. The reconstructive filter corresponding to an opening is defined by fully passing any grain not eliminated by the opening and deleting all other grains. Adaptive design results from treating the parameter vector of a reconstructive multiparameter (tau) -opening as the state space of a Markov chain. The present paper considers the relationship between Markovian queueing networks and adaptive multiparameter (tau) - openings for the signal-union-noise model.

Optical morphological SDF filter for multiple-object recognition

Abstract: A new nonlinear morphological detection algorithm is proposed for input scenes with a number of objects present in a clutter. It uses a synthetic discriminant function (SDF) to form the matched spatial filter (MSF) of the structuring element (SE) used in the hit-miss transform (HMT) detection. The SDF synthetic technique is used to adapt intraclass distortions and interclass similarity, where an HMT is used to adapt noisy and cluttered scenes. Simulation results show the proposed algorithm is an attractive nonlinear optical image processing technique that can be applied to an HMT detection to improve both the false alarm rate and its ability to detect multiple-object with distortions and clutter.

Modeling of Texture Images by the Structuring Element of Morphological Filters.

Abstract: This paper proposes a new modeling method of texture images based on morphological operations. For the texture analysis One of important texture modeling method is the linear prediction. In the linear prediction method, the frequency feature of texture images is put into autore-grssive (AR) model (i.e., all-pole linear filter) On the other hand,we would like to show new models which represent the structural feature of texture images. It is well known, the patter spectrum calculated by morphological operations are utilized the texture analysis, since morphological operations have the ability to extract structural feature. Thus, we attempt to put into structural feature of texture images put into the structural element of morphological operations. We show the effectiveness of the proposed models through the recognition test.