Structuring element

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

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.

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.

Locally adaptable mathematical morphology

Abstract: We investigate how common binary mathematical morphology operators can be adapted so that the size of the structuring element (SE) can vary across the image. We show that when the SE are balls of a metric, locally adaptable erosion and dilation can be efficiently implemented as a variant of distance transformation algorithms. Opening and closing are obtained by a local threshold of a distance transformation, followed by the adaptable dilation.

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.

Retinal vessel segmentation using Morphological Angular Scale-Space

Abstract: In this paper, segmentation of retinal vessel is done using an innovative procedure named as Morphological Angular Scale-Space (MASS). A linear structuring element rotated at different angles determines the connected components and ensuring that connectivity is not lost across the vessels. A scale-space is created by varying the length of the linear structuring element. Evolution to higher scales gradually reduces non-vessel like elements from the processing image with information from the lower scales being utilized to build the image of higher scales. The method, attains lowest mean square error value at a certain scale, which is observed to be better than most of the other morphological techniques of vessel segmentation. A publicly available DRIVE database is used for analysis of the MASS technique as well as to compare with other methods.