Showing papers on "Structuring element published in 2011"

Morphological Multiscale Enhancement, Fuzzy Filter and Watershed for Vascular Tree Extraction in Angiogram

Abstract: This paper presented an automatic morphological method to extract a vascular tree using an angiogram. Under the assumption that vessels are connected in a local linear pattern in a noisy environment, the algorithm decomposes the vessel extraction problem into several consecutive morphological operators, aiming to characterize and distinguish different patterns on the angiogram: background, approximate vessel region and the boundary. It started with a contrast enhancement and background suppression process implemented by subtracting the background from the original angiogram. The background was estimated using multiscale morphology opening operators by varying the size of structuring element on each pixel. Subsequently, the algorithm simplified the enhanced angiogram with a combined fuzzy morphological opening operation, with linear rotating structuring element, in order to fit the vessel pattern. This filtering process was then followed by simply setting a threshold to produce approximate vessel region. Finally, the vessel boundaries were detected using watershed techniques with the obtained approximate vessel centerline, thinned result of the obtained vessel region, as prior marker for vessel structure. Experimental results using clinical digitized vascular angiogram and some comparative performance of the proposed algorithm were reported.

Anisotropic Morphological Filters With Spatially-Variant Structuring Elements Based on Image-Dependent Gradient Fields

Abstract: This paper deals with the theory and applications of spatially-variant discrete mathematical morphology. We review and formalize the definition of spatially variant dilation/erosion and opening/closing for binary and gray-level images using exclusively the structuring function, without resorting to complement. This theoretical framework allows to build morphological operators whose structuring elements can locally adapt their shape and orientation across the dominant direction of the structures in the image. The shape and orientation of the structuring element at each pixel are extracted from the image under study: the orientation is given by means of a diffusion process of the average square gradient field, which regularizes and extends the orientation information from the edges of the objects to the homogeneous areas of the image; and the shape of the orientated structuring elements can be linear or it can be given by the distance to relevant edges of the objects. The proposed filters are used on binary and gray-level images for enhancement of anisotropic features such as coherent, flow-like structures. Results of spatially-variant erosions/dilations and openings/closings-based filters prove the validity of this theoretical sound and novel approach.

Morphological bilateral filtering and spatially-variant adaptive structuring functions

Abstract: Development of spatially-variant filtering is well established in the theory and practice of Gaussian filtering. The aim of the paper is to study how to generalize these linear approaches in order to introduce adaptive nonlinear filters which asymptotically correspond to spatiallyvariant morphological dilation and erosion. In particular, starting from the bilateral filtering framework and using the notion counter-harmonic mean, our goal is to propose a new low complexity approach to define spatially-variant bilateral structuring functions. Then, the adaptive structuring elements are obtained by thresholding the bilateral structuring functions. The methodological results of the paper are illustrated with various comparative examples.

Recursive structure element decomposition using migration fitness scaling genetic algorithm

Abstract: This paper proposed an improved decomposition approach for structuring elements of arbitrary shape. For the model of this method, we use the recursive model which decomposes a given structuring element into a variablesize matrix dilated by a fixed-size matrix and with union of a residue component, such procedures repeated until the variable-size matrix is smaller than a predefined threshold. For the algorithm of our method, we proposed an improved GA based on the ring topology of migration model and the power-rank fitness scaling strategy. The experiments demonstrate that our method is more robust than Park's method, Anelli's method, and Shih's method, and gave the final decomposition tree of different SE shapes such as the letter "V", heart, and umbrella.

Intelligent FPGA based system for shape recognition

Abstract: Mathematical morphology supplies powerful tools for low level image analysis, with applications in many areas. In this paper, the development of a novel reconfigurable hardware using a genetic algorithm and a pipeline architecture is proposed for the task of shape recognition in binary images. For the recognition process, a large sized convex structuring element representing the object shape to be recognized is decomposed into the architecture stages. Each stage can handle structuring elements of a limited size. In this approach, a genetic algorithm was used to decompose this structuring element. Thus, a simple erosion performed in each stage is used to detect the goal object. The hardware is capable of processing binary images at high speed. The developed system is based on FPGAs. Our approach represents an intelligent mechanism to reconfigure the pipeline architecture, it is different from other systems found in the literature, and the obtained results are promising.

A simple methodology to segment x-ray tomographic images of a multiphasic building stone

Abstract: Assessment of the weathering of a particular limestone, the tuffeau, used in historical monuments requires an accurate description of its microstructure. An efficient tools to obtain such a description is X-ray microtomography. However the segmentation of the images of this multiphasic material is not trivial. As the identification of pertinent markers of the structural components to extract is difficult, a two steps filtering approach is chosen. Alternate sequential filters are shown to efficiently remove the noise but, as they destroy the structural components smaller than the structuring element used, they cannot be carried out far enough. Hence as a second step in the filtering process, a mosaic operator, relying on a pragmatic yet efficient marker determination, is implemented to simplify further the images.

Region Growing Structuring Elements and New Operators Based on Their Shape

Abstract: This paper proposes new adaptive structuring elements in the framework of mathematical morphology. These structuring elements (SEs) have a fixed size but they adapt their shape to the image content by choosing, recursively, similar pixels in gray-scale, with regard to the seed pixel. These new SEs are called region growing structuring elements (REGSEs). Then, we introduce an original method to obtain some features by analyzing the shape of each REGSE. We get a powerful set of operators, which is able to enhance efficiently thin structures in an image. We illustrate the performance of the proposed filters with an application: the detection of cracks in the framework of non-destructive testing. We compare these methods with others, including morphological amoebas and general adaptive neighborhood structuring elements and we see that these operators, based on REGSE, yield the best detection for our application.

The gray level aura matrices for textured image segmentation

Abstract: Inspired by an intuitive analogy that exists between the gray level textures and the miscibility in the multiphase fluids, the aura concept was developed from set theory tools in order to modeling the texture image. The gray level aura matrix (GLAM) has been then proposed to generalize the gray level cooccurrence matrix (GLCM) which remains very popular in the texture analysis. The GLAM indicates how much each gray level is present in the neighborhood of each other gray level. The neighborhood is defined by a structuring element as one used in mathematical morphology. The GLAM is mainly used and studied in synthesis and classification of textures framework but very few works are devoted to the segmentation. The aim of this paper is to exploit the GLAM for the segmentation of textured images. Experiments results over synthetic and real images show the efficiency of the GLAM. The influence of the shape and the size of the structuring element on the segmentation results are also studied.

Highly Accurate Schemes for PDE-Based Morphology with General Convex Structuring Elements

Abstract: The two fundamental operations in morphological image processing are dilation and erosion. These processes are defined via structuring elements. It is of practical interest to consider a variety of structuring element shapes. The realisation of dilation/erosion for convex structuring elements by use of partial differential equations (PDEs) allows for digital scalability and subpixel accuracy. However, numerical schemes suffer from blur by dissipative artifacts. In our paper we present a family of so-called flux-corrected transport (FCT) schemes that addresses this problem for arbitrary convex structuring elements. The main characteristics of the FCT-schemes are: (i) They keep edges very sharp during the morphological evolution process, and (ii) they feature a high degree of rotational invariance. We validate the FCT-scheme theoretically by proving consistency and stability. Numerical experiments with diamonds and ellipses as structuring elements show that FCT-schemes are superior to standard schemes in the field of PDE-based morphology.

Non Uniform Background Removal using Morphology based Structuring Element for Particle Analysis

Abstract: Non Uniform Illumination in an image often leads to diminished structures and inhomogeneous intensities of the image due to different texture of the object surface and shadows cast from different light source directions. This effect is adverse in case of biological images. Techniques such as segmentation, edge detection and contrast or brightness enhancement using Histogram Equalization could not differentiate between some of the particles and their background or neighboring pixels. This paper is aimed to remove these problems in microscopic image processing by removing the problem of non-uniform background illumination from the image using Morphological Opening, Adaptive Histogram Equalization and Edge detection techniques for particle analysis .A comparative study have been shown and a new algorithm is proposed for removing the problem of non-uniform background illumination in biological images for visualizing and estimation of growth of fungus in a particular sample to transform the input image to its indexed form with maximum accuracy involving morphological openings and structuring element design using Morphological Processing.

Recursive multi-SEs NWTH method for small target detection in infrared images

Abstract: Mathematical morphology techniques, such as white top-hat (WTH) and new white top-hat (NWTH) transformation have been researched for small target detection application in the infrared (IR) images. However, its target enhancement performance still depends on its SE size set by users. To solve this problem, we propose a recursive multi-structuring elements (multi-SEs) NWTH method with an automatic decision mechanism of the SE size. The proposed method based on the NWTH transformation updates the multi-SEs by calculating candidate target-to-clutter ratio gain (CTCRG) of the NWTH images by the multi-SEs. Through a recursive procedure, final multi-SEs is automatically selected, and then small targets can be detected in a summation image of NWTH images by the final multi-SEs.

Fast streaming algorithm for 1-D morphological opening and closing on 2-D support

Abstract: This paper presents a new streaming algorithm for 1-D morphological opening and closing transformations on 2-D support. Thanks to a recursive computation technique, the algorithm processes an image in constant time irrespective of the Structuring Element (SE) size, with a minimal latency and very low memory requirements, supporting various input data types. It reads and writes data strictly sequentially in the same (horizontal) scan order for both the horizontal and vertical SE. Aforementioned properties allow an efficient implementation in embedded hardware opening a new opportunity of a parallel computation.

Morphological filters based on motif combination for functional surface evaluation

Abstract: Regarded as the complement of commonly used mean-line based filters, morphological filters are more function oriented. They are relevant to geometrical properties of surfaces and provide better results for functional evaluation of surfaces. The paper first gives a brief introduction to morphological filters. An algorithm to implement morphological filters is proposed, which is based on the motif combination. Experimental data are presented to illustrate the algorithm's superiority in performance. The end effect of morphological filters is corrected by the reflective padding. Either circular or horizontal structuring element is available using this method. Two examples of applying the morphological closing filter with the disk and the line-segment structuring element on a milled surface profile are illustrated. Finally, the morphological alternating sequential filter is employed to evaluate the roughness of functional stratified surfaces as a replacement to the two-stage Gaussian filter.

Robust long range target detection algorithm using adaptive selective top-hat transform

Abstract: Airborne threats like aircrafts and missiles at long ranges appear as point or small targets in visible and infra-red image sequences. Size of the target in input image increases as it approaches towards the imaging sensor. Mathematical morphology is used to detect such targets but its performance is highly dependent on shape and size of structuring element. In this paper, we propose a novel robust algorithm for detection of long range approaching targets in visible and infra-red image sequences. The proposed detection algorithm is based on the novel adaptive selective double structuring element top-hat transform (Adapt-Sel-DSTHT) and maximum correlation criterion. A switching logic based on variance of the input image is used to apply the additional rule to modify the filtered image for highly cluttered images. Results demonstrate high probability of detection and low false alarm even for highly clouded scenario. Reduced number of operations led to suitability for real time implementation.

Wood Defect Detection Based on Mathematical Morpholog

Abstract: This paper introduces the method of gray-scale morphological filtering based on mathematical morphology,multi-scale morphological structuring elements defined in order to adapt to different edge in the image,followed with examples of a wood defect image treated after mathematical morphological operation.According to the characteristics of wood internal defects,analyzing the selection of structure elements,the author presents a method of edge detection which combined with double structuring element and morphology filtering that based on mathematical morphology.The experiments show that the method not only improved the reliability of internal defects detection of wood,strengthened the pre-segmentation image visibility and integrity,but also improved the precision of the defect extraction.

Using mathematical morphology for similarity search of 3D objects

Abstract: In this paper we use the erosion and dilation operators for characterizing 3D polygonal objects. The goal is to perform a similarity search in a set of distinct objects. The method applies successive dilations and erosions of the meshes in order to compute the difference volume as a function of the size of the structuring element. Because of appropriate pre-processing, the resulting function is invariant to translation, rotation and mesh resolution. On a set of 32 complex objects with different mesh resolutions, the method achieved an average ranking rate of 1.47, with 23 objects ranked first and 6 objects ranked second.

A Survey Paper on: Fuzzy Mathematical Morphology Techniques for Digital Image Processing

Abstract: This paper puts across the various approaches and methods that have been proposed in the context of Fuzzy Mathematical Morphology. The underlying principles of Dilation & Erosion, the structuring elements used in various techniques, the unique variations put forth by researchers, new applications in spatial relationships, decision making, segmentation of medical images have been discussed.

Singular value decomposition method for the detection of defects in woven fabric refined by morphological operation

Abstract: In this paper a new approach for the detection of defects in woven fabric is presented where the singular value decomposition (SVD) method is used. SVD basically removes the interlaced grating structure of the waft and warp of the fabric leaving aside the defective part of the fabric. An intensity threshold value along with the module of definite size is considered for the binarization of the background free fabric image. Finally, for the removal of the noise from the binary fabric image the morphological opening operation with the suitable structuring element is performed. The technique is tested on 287 fabric samples consisting of five different types of defects in three types of woven fabrics from TILDA database. 94.08% success rate of detection of defects is achieved.

Primitive and grain estimation using flexible magnification for a morphological texture model

Abstract: An improved morphological estimation method of textural elements based on the Primitive, Grain, and Point Configuration (PGPC) texture model is proposed. The PGPC texture model has shown promising applications such as noise removal, texture modification, and texture synthesis. However, the estimation is not always successful since the magnification process of the primitive to ensure the assumption that the grains are homothetic does not always fit to each image. We propose in this paper a novel estimation method introducing more flexibility into homotheticity's conventional assumption of the grains, and exploring a suitable structuring element for the homothetic magnification process of the primitive. Experimental results show that the proposed method provides more representative grains than the conventional method

Fingerprint Image Enhancement Based on Morphological Filter

Abstract: Fingerprint image enhancement is a essential preprocessing step to ensure good performance in Automatic Fingerprint Identification System (AFIS). In this paper, we proposed a new fingerprint enhancement algorithm based on morphological filter. This algorithm first employs the idea of median filtering based on the Mehtre method to obtain a reliable ridge orientation field. And then it designes a morphological structuring element database to save structuring element templates of all directions. Finally, in order to remove the empty and rupture in the ridge, the morphological closing operation is employed through selecting the corresponding template according to the local ridge orientation. We have tested the algorithm on FVC2004 fingerprint database DB2. Experimental results indicate that the proposed algorithm can obtain great results at both the orientation field estimation and the minutiae detection performance.

Object descriptors based on a list of rectangles: method and algorithm

Abstract: Most morphological operators use a unique structuring element, possibly at different scales, to describe an object. In addition, morphological algorithms are often restricted to 1D structuring elements, combinations of 1D elements, or isotropic structuring elements (like circles), because of the lack of methods directly applicable to arbitrary shaped 2D structuring elements. While these descriptors have proved useful in the past, we propose an alternative that uses the list of maximal rectangles contained in a set X. In particular, we focus on an opening that preserves large rectangles contained in a set X and on its companion 2D algorithm that builds a list of all the maximal rectangles that fit inside an arbitrary set X. This list is the base of new descriptors that have been used successfully for machine learning tasks related to the analysis of human silhouettes. For convenience, we provide the C source code and a program of our algorithm at http://www.ulg.ac.be/telecom/rectangles

Insulator ultraviolet discharge feature extraction based on level set

Abstract: For obtainment of facula area of external insulation ultraviolet discharge figure automatically and accurately, a facula area extraction method based on active contour models was proposed in the paper. Of the active contour models, the ultraviolet discharge region contour was obtained by calculation of partial differential equation. The algorithm has global optimization capacity, which is different from obtainment of edge by local figure information of conventional methods. It has strong capacity of resisting disturbance, low requirement for image definition, in the meantime, need not preprocessing, the facula area of ultraviolet discharge could be obtained accurately. Conventional edge detection operators, mathematical morphology method and the proposed method were implemented to obtain facula area of ultraviolet discharge figure, the result indicates that: the edge obtained by the conventional edge detection operators exist much interference, and the obtained contour points is discrete, it is difficult to obtain the facula area. The proposed method can obtain single or multiple facula area accurately, and it has not the optimal threshold selection problem and dimension selection of structuring element problem of mathematical morphology method, has strong automation capacity.

Adaptive morphological filter to fault diagnosis of gearbox

Abstract: Focusing on fault diagnosis extraction of gearbox, a novel approach is proposed according to the signal characteristics based on the adaptive morphological filter. Traditional linear filters have some limitations when extracting nonlinear features. As a nonlinear analysis method, the morphological filter has better performance on detail reservation and noise reduction, and can describe nonlinear morphological characteristics more clearly than linear filters. The structuring element (SE) of the morphological filter is similar to the window function of the linear filter. In order to avoid the drawbacks of the ambiguity of the selecting of SEs and the dependence on empirical rules, an adaptive morphological filter is proposed based on kurtosis maximization principle in this paper. The experimental results show that the adaptive algorithm can be more efficient to extract fault features of gearbox.

A method to determine the size of the structuring element in morphological correction of non-uniform illumination

Abstract: Correction of uneven illumination in images is frequently used as a preprocessing step, for which morphological processing is a well established technique. This task has some specific characteristics for images containing disjoint blobs. These images appear frequently in applications like cellular imaging, where it is often necessary to process them in large amounts. In such cases, achieving a high degree of automation becomes an important factor. When correcting uneven illumination by morphological processing for this class of images, selecting an appropriate size for the structuring element (SE) is a relevant issue. In this work, a method based in grayscale morphological operations is presented, to determine an appropriate size of the SE. The effectiveness of the method was tested through simulations using images derived from real world examples. Results are presented in terms of tables showing the precision in recovering the original image intensities, as well as through illustrative images.

Radar target classification using morphological image processing

Abstract: Morphological operators are commonly used in image processing. We study their suitability for use in synthetic aperture radar (SAR) image enhancement and target classification. Morphological operations are nonlinear operators defined by set theory. The dilation and erosion operations grow or shrinkimage features that match to a predefined structuring element. The opening and closing operations are combinations of successive dilation and erosion. These morphological operations can visually emphasize scattering of interest in an image. We investigate whether these operations can also improve target classification performance. The operators are nonlinear and image dependent; thus we cannot predict performance without empirical testing. We test and evaluate the morphological operators using simulated and measured SAR data. Results show the dilation operator is most promising for increasing match score and separation between classes in the decision space.

A Novel Improved Edge Detection Method

Abstract: In this paper, a novel morphological edge detection using adaptive weighted morphological operators is presented. The newly introduced operators employ weighted structuring element (SE) and apply multiplication or division in place of addition and subtraction in classical morphological operations. It judges its edge and its direction by means of training method and differentiable equivalent representations for the operators, efficient adaptive algorithms to optimize SEs are derived. The gradient of the adaptive weighted morphology utilizes a set of SEs to detect the edge strength with a view to decrease the spurious detail edge and suppressed the noise. Results will be presenting for images in comparison with the others edging detectors.