Structuring element

About: Structuring element is a research topic. Over the lifetime, 997 publications have been published within this topic receiving 26839 citations.

A Signal Based Triangular Structuring Element for Mathematical Morphological Analysis and Its Application in Rolling Element Bearing Fault Diagnosis

Abstract: Mathematical morphology (MM) is an efficient nonlinear signal processing tool It can be adopted to extract fault information from bearing signal according to a structuring element (SE) Since the bearing signal features differ for every unique cause of failure, the SEs should be well tailored to extract the fault feature from a particular signal In the following, a signal based triangular SE according to the statistics of the magnitude of a vibration signal is proposed, together with associated methodology, which processes the bearing signal by MM analysis based on proposed SE to get the morphology spectrum of a signal A correlation analysis on morphology spectrum is then employed to obtain the final classification of bearing faults The classification performance of the proposed method is evaluated by a set of bearing vibration signals with inner race, ball, and outer race faults, respectively Results show that all faults can be detected clearly and correctly Compared with a commonly used flat SE, the correlation analysis on morphology spectrum with proposed SE gives better performance at fault diagnosis of bearing, especially the identification of the location of outer race fault and the level of fault severity

Morphological decomposition of sandstone pore–space: fractal power-laws

Abstract: Morphological decomposition procedure is applied to estimate fractal dimension of a pore–space, which is isolated from a sandstone microphotograph. The fractal dimensions that have been computed by considering various probing rules have precisely followed the universal power-law relationships proposed elsewhere. These results are derived by considering structuring elements such as octagon, square and rhombus that have been used to decompose the pore– space of sandstone image. The radii of the structuring elements are made to increase in a cyclic fashion. To perceive the decomposed pore image, a color-coding scheme is adapted, from which one can identify several sizes of these structuring elements that could be fit into this pore. This exercise facilitates testing of the relationship between the radius of the structuring elements that could be used to decompose the pore at different levels, and the number of decomposed shapes that could be fit into the pore while using the corresponding structuring element. From the number–radius relationship, the fractal dimensions of pore–space estimated, by considering these structuring elements, yield the values of 1.82, 1.76, and 1.79. These values are in conformity with the values arising from estimation of box dimension method, as well as the dimensions of the corresponding pore connectivity networks (PCNs). 2003 Elsevier Science Ltd. All rights reserved.

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.

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.

Linear combinations of morphological operators: the midrange, pseudomedian, and LOCO filters

Abstract: It is shown how linear combinations of morphological operators can be formed to alleviate the bias introduced by the individual morphological operators. Since every morphological operator has a complementary operator that is equally and oppositely biased, the authors propose averaging the complementary operators to alleviate the bias. Of the three filters formed by averaging the standard morphological operators, two are the previously defined midrange filter, and pseudomedian filter, while one is a new filter, called the LOCO filter. Under most conditions, the LOCO filter is the best of these at reducing impulses and noise., Linear combinations of morphological operators allow the shape control of the morphological filters (exerted by the selection of a structuring element) without introducing bias. For example, the LOCO filter with a square structuring element preserves 90 degrees corners in an image while reducing noise almost as well as the square-shaped medium filter, which rounds off such corners. >