Showing papers on "Structuring element published in 2023"

Improved Self-Sparse Fuzzy Clustering Algorithm for Image Segmentation

Abstract: The effectiveness of the FCM image segmentation algorithm can be increased by introducing local spatial information as FCM is very sensitive to noise. Also, during transmission, the image details are lost because noise is added. Thus, an Alpha-trimmed mean filter is utilized as a pre-processing step to maintain the image features and immunity. Then SSFCA algorithm is used to obtain a fuzzy membership with sparsity. It is used to reduce the non-homogeneous interference and got a good segmentation result in comparison to other self-optimized FCM algorithms. Again, to overcome the background noise the resultant image is applied to the morphological operators as a postprocessing step. Experiments show that this method can create good segmentations.

Properties of Morphological Dilation in Max-Plus and Plus-Prod Algebra in Connection with the Fourier Transformation

Abstract: Abstract The basic filters in mathematical morphology are dilation and erosion. They are defined by a structuring element that is usually shifted pixel-wise over an image, together with a comparison process that takes place within the corresponding mask. This comparison is made in the grey value case by means of maximum or minimum formation. Hence, there is easy access to max-plus algebra and, by means of an algebra change, also to the theory of linear algebra. We show that an approximation of the maximum function forms a commutative semifield (with respect to multiplication) and corresponds to the maximum again in the limit case. In this way, we demonstrate a novel access to the logarithmic connection between the Fourier transform and the slope transformation. In addition, we prove that the dilation by means of a fast Fourier transform depends only on the size of the structuring element used. Moreover, we derive a bound above which the Fourier approximation yields results that are exact in terms of grey value quantisation.

The Polynomial Connection between Morphological Dilation and Discrete Convolution

Abstract: In this paper we consider the fundamental operations dilation and erosion of mathematical morphology. Many powerful image filtering operations are based on their combinations. We establish homomorphism between max-plus semi-ring of integers and subset of polynomials over the field of real numbers. This enables to reformulate the task of computing morphological dilation to that of computing sums and products of polynomials. Therefore, dilation and its dual operation erosion can be computed by convolution of discrete linear signals, which is efficiently accomplished using a Fast Fourier Transform technique. The novel method may deal with non-flat filters and incorporates no restrictions on shape or size of the structuring element, unlike many other fast methods in the field. In contrast to previous fast Fourier techniques it gives exact results and is not an approximation. The new method is in practice particularly suitable for filtering images with small tonal range or when employing large filter sizes. We explore the benefits by investigating an implementation on FPGA hardware. Several experiments demonstrate the exactness and efficiency of the proposed method.

Morphological Sampling Theorem and its Extension to Grey-value Images

Abstract: Sampling is a basic operation in image processing. In classic literature, a morphological sampling theorem has been established, which shows how sampling interacts by morphological operations with image reconstruction. Many aspects of morphological sampling have been investigated for binary images, but only some of them have been explored for grey-value imagery. With this paper, we make a step towards completion of this open matter. By relying on the umbra notion, we show how to transfer classic theorems in binary morphology about the interaction of sampling with the fundamental morphological operations dilation, erosion, opening and closing, to the grey-value setting. In doing this we also extend the theory relating the morphological operations and corresponding reconstructions to use of non-flat structuring elements. We illustrate the theoretical developments at hand of examples.

Analysis of Blood Smear Images Using Dark Contrast Algorithm and Morphological Filters

Abstract: In recent years, Biomedical Imaging has emerged as an effective tool in diagnosis of various diseases. In order to perform anatomy or histology of cells, Blood Smear Images are used. To process these images, enhancement plays a major role in order to increase visual quality of the image and for accurate segmentation of Region of Interest (ROI). The motive of this work is to perform enhancement using the Dark Contrast Algorithm (DCA) since it increases the intensity of darker regions, which in case of Blood Smear Images are nucleus. Further, the quality of enhanced image is evaluated using suitable Image Quality Assessment (IQA) metric. This enhanced image is segmented using Morphological Filters with appropriate structuring element to extract ROI which is nucleus and cell periphery. This helps to identify irregularities in cell periphery to detect various blood disorders. The performance of segmentation technique is assessed using Jaccard Coefficient (JC).