Structuring element

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

Digital processing of an iris image

Abstract: The method involves filtering an image with a structuring element corresponding to a disk with a diameter approximately corresponding to a thickness of an eyelash. A structuring element corresponding to a disk having a radius corresponding to a minimum expected radius of a pupil in the image is opened. A contrast enhancement comprising a linear piecewise stretching is enhanced to adjust histogram of grey levels of the image within a bounded interval, defined by the minimum and maximum intensity values desired in the final image. An independent claim is also included for a system for recognizing an iris of an eye.

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.

Optimized maximum principal curvatures based segmentation of blood vessels from retinal images

Abstract: In retinal image of the human eye, extracting tree shaped retinal vasculature is an important feature which helps eye care specialists or ophthalmologists to pursue proper diagnostic procedures. In this paper, an approach named Optimized Maximum Principal Curvatures Based (OPCB) segmentation is been proposed for efficient extraction of blood vessels from retinal fundus images. This algorithm proceeds into two stages. Firstly, pre-processing on input retinal images is done by Particle Swarm Optimization (PSO) technique which is an automatic process for computing the global optimum pixels of the image in order to avoid working with all or random pixels. Later, these optimal pixels are made to undergo further processing with Gaussian Filter to remove the noisy pixels among them. Secondly, the post-processing is carried out in four steps: (i)Maximum Principal Curvatures (maximum-eigenvalues) of the second order derivative matrix (Hessian) quantity of the pre-processed PSO image are computed by using ‘Lambda Function’, which then does region growing of the tree-shaped blood vessels by convolving Maximum Principal Curvatures with the mathematical erosion structuring element of. (ii)After extraction of blood vessels, section-wise contrast enhancement is performed by using Adaptive Histogram Equalization that work on 8x8 tiles of image being segmented for smoothing artificially introduced boundaries if any, and also for eliminating over amplified noise. (iii) ISODATA (Iterative Self-Organizing Data Analysis Technique) thresholding is used to classify the image globally where the image’s foreground vascular structure is segmented from the background. (iv) A ‘morphologically opened’ operation is performed to prune falsely segmented isolated regions, to achieve very accurate segmentation. This proposed technique tested online available colored retinal images of STARE and DRIVE databases. As an outcome, the proposed approach achieves the superior segmentation accuracy of 96% which outperformed many empirically proven segmentation methods which were proposed in the past.

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.