Structuring element

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

Fast implementation of morphological operations using binary image block decomposition

Abstract: Morphological transformations are commonly used to perform a variety of image processing tasks. However, morphological operations are time-consuming procedures since they involve ordering and min/max computation of numbers resulting from image interaction with structuring elements. This paper presents a new method that can be used to speed up basic morphological operations for binary images. To achieve this, the binary images are first decomposed in a set of non-overlapping rectangular blocks of foreground pixels that have predefined maximum dimensions. Then off-line dilation and erosion of all rectangular blocks are arbitrary obtained and stored into suitable look-up array tables. By using the look up tables, the results of the morphological operations to the rectangular blocks are directly obtained. Thus, first all image blocks are replaced by their look-up array tables. Then the morphological operations are applied only to the limited number of the remaining pixels. Experimental results reveal that starting from a block represented binary image morphological operations can be executed with different types of structuring elements in significantly less CPU time. Using the block representation, we are able to perform dilation 16 times faster than non-fast implementations and 10 times faster than an alternative fast implementation based on contour processing. Significant acceleration is also recorded when using this approach for repeated application of dilation (for 10 iterations, dilation using the block representation is over 20 times faster than non-fast implementations and over four times faster than using the fast contour based approach).

Morphological Transformation in Color Space-Based Edge Detection of Skin Lesion Images

Abstract: Detection of lesion border from skin image is very essential for developing efficient non-invasive computer-aided diagnosis. For creation of an automatized system, edge detection is an essential step. An improved color space-based edge detection method with linear structure element is introduced in this paper. At first, the input image is converted to red color space and the Sobel operator is applied to detect the border. After this, linear structuring element is introduced, followed by filling up of holes to get appropriate segmented image. The performance of this method is measured by comparing its result with the results obtained by using a standard Sobel edge detection method.

Structuring Elements in the Watershed Algorithm for the Segmentation of Mammography Images

Abstract: Reading mammography images has always been a challenging task even for experienced radiologists. With the advancements in computer technology, machine tools such as the Computer Aided Detection and Diagnosis (CAD) systems are widely engaged as a second reader to assist radiologists in image reading. One of the important processes in the CAD machine is the segmentation process. The morphological watershed algorithm is one of the hybrid technique that combines boundary and region criteria, but this algorithm has several drawbacks such as over-segmentation and sensitive to noise. In this research, the denoising method applies the Principal Component Analysis (PCA) filtering. Prior to the segmentation by the watershed algorithm, the Fuzzy C-Means (FCM) clustering algorithm is used to identify the image foreground, which is the region of interest (abnormality region). A marker-controlled watershed algorithm is implemented to overcome the over-segmentation drawback. Furthermore, applying a suitable shape of structuring element in the watershed algorithm has the same effect of reducing the over-segmentation problem. Thus, three shapes of structuring elements, which are the disk, diamond, and octagon are tested and compared. The aim of this research is to find a suitable shape of structuring element for the marker-controlled watershed algorithm. For the evaluation of the segmentation performance, three evaluation methods are used, which are the Jaccard Index (JI), Dice Similarity Coefficient (DSC) and Figure of Merit (FOM). The result of the comparison shows that the diamond-shaped structuring element is a suitable shape for the segmentation of mammography images.

Structuring element in detection of IR images based on soft mathematical morphology

Abstract: There are low target-to-background contrast, blurred edge and stronger noise in the infrared images which are always used in precise guidance. When applied to process the images, the traditional methods cannot get better processing result, because it cannot compress the noise effectively. But the soft mathematical morphological filter is non-linear filter and has many merits such as less sensitivity, higher computation speed etc. So it is well adapted to infrared images processing. The main performance of the soft morphological filter depends on different morphological exchange types and structuring elements. Unfortunately, there are no analytical criteria for choosing these structuring parameters. In order to obtain better image processing result, we must choose the most suitable structuring system based on specific task of image processing. In this paper, we take advantage of the profile and IR properties of ship to determine the shape and size of the structuring elements, and use the threshold theory to determine the values of structuring elements. The structuring system is applied to actual IR ship images. Some processing results are also listed in this paper. After the comparing of different results, a suitable structuring system is given at last.

A Fractal-Based Approach to Network Characterization Applied to Texture Analysis

Abstract: This work proposes a new method for texture analysis that combines fractal descriptors and complex network modeling. At first, the texture image is modeled as a network. Then, the network is converted into a surface where the Cartesian coordinates and the vertex degree is mapped into a 3D point in the surface. Then, we calculate a description vector of this surface using a method inspired by the Bouligand-Minkowski technique for estimating the fractal dimension of a surface. Specifically, the descriptor corresponds to the evolution of the volume occupied by the dilated surface, when the radius of the spherical structuring element increases. The feature vector is given by the concatenation of the volumes of the dilated surface for different radius values. Our proposal is an enhancement of the classic complex networks descriptors, where only the statistical information was considered. Our method was validated on four texture datasets and the results reveal that our method leads to highly discriminative textural features.