Structuring element

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

Medical feature based evaluation of structuring elements for morphological enhancement of ultrasonic images.

Abstract: This paper investigates the use of morphology-based nonlinear filters, and performs deterministic and statistical analysis of the linear combinations of the filters for the image quality enhancement of B-mode ultrasound images. The fact that the structuring element shape greatly influences the output of the filter, is one of the most important features of mathematical morphology. The present reported work comparatively evaluates the structuring elements for morphological liver image enhancement and verifies the hypothesis that the speckles visible in US images are short, slightly ‘banana-shaped’ white lines. Initially, five different liver images were morphologically filtered using 10 different structuring elements and then the filtered images were assessed quantitatively. Image quality parameters such as peak signal-to-noise ratio, mean square error and correlation coefficient have been used to evaluate the performance of the morphological filters with different structuring elements. To endorse the obser...

A hardware oriented ad-hoc computer-based method for binary structuring element decomposition based on genetic algorithms

Abstract: Mathematical morphology (MM) is a popular formalism largely used for image processing. Of particular relevance in MM-based operations is the structuring element (SE). In an image processing environment SE defines which pixel values, in the input image, to include in the calculation of the output value. Most MM-based image processing environments employ limited size SEs which prevents their use in tasks requiring larger SEs. This paper proposes a computer-based method for optimizing the decomposition of SEs, in binary image related tasks, that employs binary MM, which automatically transforms an original SE into a corresponding sequence of 3 × 3 SEs. The decomposition operation reduces the complexity of the morphological operations and has been implemented as a genetic algorithm (GA) based process, that searches for the best sequence of smaller structuring elements, using one dilation and four union operations, for the decomposition of each large-sized structuring element. By using a GA with a fixed-length chromosome as well as a fixed number of dilation and union operations, the method has a simple and fixed structure, which makes it a convenient choice for hardware implementations. Its performance, based on six images already used in the literature by other well-established method, has shown to be competitive.

Horizontal and vertical line detection and removal for document images

Abstract: A vertical and horizontal line detection method for document images includes generating multiple binary images from the input grayscale document image based on multiple binarization thresholds, detecting horizontal and vertical lines in each of the multiple binary images independently, and merging the detection results from the multiple binary images. The line detection process for each binary image include applying an opening operation using a vertical or horizontal line as the structuring element, and removing connected components that are not vertical or horizontal lines based on a stroke width analysis. The boundaries of the detected lines are obtained using horizontal and vertical projections.

Clustering-driven watershed adaptive segmentation of bubble image

Abstract: In order to extract froth morphological feature, a bubble image adaptive segmentation method was proposed. Considering the image’s low contrast and weak froth edges, froth image was coarsely segmented by using fuzzy c means (FCM) algorithm. Through the attributes of size and shape pattern spectrum, the optimal morphological structuring element was determined. According to the optimal parameters, some image noises were removed with an improved area opening and closing by reconstruction operation, which consist of image regional markers, and the bubbles were finely separated from each other by watershed transform. The experimental results show that the structural element can be determined adaptively by shape and size pattern spectrum, and the froth image is segmented accurately. Compared with other froth image segmentation method, the proposed method achieves much high accuracy, based on which, the bubble size and shape features are extracted effectively.

Mathematical morphology directly applied to point cloud data

Abstract: Many of the point cloud processing techniques have their origin in image processing. But mathematical morphology, despite being one of the most used image processing techniques, has not yet been clearly adapted to point clouds. The aim of this work is to design the basic operations of mathematical morphology applicable to 3D point cloud data, without the need to transform point clouds to 2D or 3D images and avoiding the associated problems of resolution loss and orientation restrictions. The object shapes in images, based on pixel values, are assumed to be the existence or absence of points, therefore, morphological dilation and erosion operations are focused on the addition and removal of points according to the structuring element. The structuring element, in turn, is defined as a point cloud with characteristics of shape, size, orientation, point density, and one reference point. The designed method has been tested on point clouds artificially generated, acquired from real case studies, and the Stanford bunny model. The results show a robust behaviour against point density variations and consistent with image processing equivalent. The proposed method is easy and fast to implement, although the selection of a correct structuring element requires previous knowledge about the problem and the input point cloud. Besides, the proposed method solves well-known point cloud processing problems such as object detection, segmentation, and gap filling.