The Bonferroni mean-type pre-aggregation operators construction and generalization: Application to edge detection

Hait, Swati Rani; Mesiar, Radko; Gupta, Pragya; Guha, Debatosh; Chakraborty, Debjani

doi:10.1016/j.inffus.2021.11.002

Cited by 19 publications

(6 citation statements)

References 38 publications

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“…In addition, this operator is based on controlling the image with a small separable and integer filter in horizontal and vertical directions. In addition, this operator uses two kernels to calculate the approximations of the derivatives attached to the original image [ 22 ]. They are one horizontally and the other vertically.…”

Section: Methodsmentioning

confidence: 99%

“…Then the gradient can be defined as follows . Using individual differences, the Robert operator determines the total of the squares of differences between diagonally adjacent pixels [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

“…Sobel operator is based on converting images in horizontal and vertical directions with a small, divisible and integer value filter, it is reasonably simple to calculate [ 22 ]. The computation is as follows: Then the gradient can be defined as .…”

Section: Methodsmentioning

confidence: 99%

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Generalized fractal dimensions based comparison analysis of edge detection methods in CT images for estimating the infection of COVID-19 disease

Thangaraj

Easwaramoorthy

2022

Eur. Phys. J. Spec. Top.

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The coronavirus, also known as COVID-19, has become highly contagious and has been associated with one of the world’s deadliest diseases. It also has direct effects on human lungs, causing significant damage. CT-scans are commonly employed in such circumstances to promptly evaluate, detect, and treat COVID-19 patients. Without any filtering, CT-scan images are more difficult to identify the damaged parts of the lungs and determine the severity of various diseases. In this paper, we use the multifractal theory to evaluate COVID-19 patient’s CT-scan images to analyze the complexity of the various patient’s original, filtered, and edge detected CT-scan images. To precisely characterize the severity of the disease, the original, noisy and denoised images are compared. Furthermore, the edge detection and filtered methods called Robert, Prewitt, and Sobel are applied to analyze the various patient’s COVID-19 CT-scan images and examined by the multifractal measure in the proposed technique. All of the images are converted, filtered and edge detected using Robert, Prewitt, and Sobel edge detection algorithms, and compared by the Generalized Fractal Dimensions are compared. For the CT-scan images of COVID-19 patients, the various Qualitative Measures are also computed exactly for the filtered and edge detected images by Robert, Prewitt, and Sobel schemes. It is observed that Sobel method is performed well for classifying the COIVD-19 patients’ CT-scans used in this research study, when compared to other algorithms. Since the image complexity of the Sobel method is very high for all the images and then more complexity of the images contains more clarity to confirm the COVID-19 images. Finally, the proposed method is supported by ANOVA test and box plots, and the same type of classification in experimental images is explored statistically.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Generalized fractal dimensions based comparison analysis of edge detection methods in CT images for estimating the infection of COVID-19 disease

Thangaraj

Easwaramoorthy

2022

Eur. Phys. J. Spec. Top.

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“…The idea of this method is to select features through clustering that contribute to the desired detection. In [97], one has an edge detection algorithm based on a Bonferroni Meantype pre-aggregation operator (BM), with more emphasis given to feature image extraction. A comprehensive comparative study was conducted to evaluate the results obtained using the proposed edge detection algorithm with some other well-known and widely used edge detectors in the literature, demonstrating the superiority of the proposed method regarding the aspects of accuracy and edge continuity.…”

Section: Definition 1 (C T -Integral)mentioning

confidence: 99%

Systematic Review of Aggregation Functions Applied to Image Edge Detection

Amorim

Dimuro

Borges

et al. 2023

Axioms

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Edge detection is a crucial process in numerous stages of computer vision. This field of study has recently gained momentum due to its importance in various applications. The uncertainty, among other characteristics of images, makes it difficult to accurately determine the edge of objects. Furthermore, even the definition of an edge is vague as an edge can be considered as the maximum boundary between two regions with different properties. Given the advancement of research in image discontinuity detection, especially using aggregation and pre-aggregation functions, and the lack of systematic literature reviews on this topic, this paper aims to gather and synthesize the current state of the art of this topic. To achieve this, this paper presents a systematic review of the literature, which selected 24 papers filtered from 428 articles found in computer databases in the last seven years. It was possible to synthesize important related information, which was grouped into three approaches: (i) based on both multiple descriptor extraction and data aggregation, (ii) based on both the aggregation of distance functions and fuzzy C-means, and (iii) based on fuzzy theory, namely type-2 fuzzy and neutrosophic sets. As a conclusion, this review provides interesting gaps that can be explored in future work.

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“…Image segmentation is the first step of image analysis which separates an image into several regions with similar properties. Several segmentation approaches have been suggested in the literature, such as edge-based [ 15 , 19 , 26 , 44 , 47 ], region-based [ 12 , 13 , 22 , 41 , 46 ], deep-learning [ 21 , 38 ], and histogram thresholding [ 8 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

An efficient image segmentation method based on expectation maximization and Salp swarm algorithm

Ehsaeyan

2023

Multimed Tools Appl

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Multilevel image thresholding using Expectation Maximization (EM) is an efficient method for image segmentation. However, it has two weaknesses: 1) EM is a greedy algorithm and cannot jump out of local optima. 2) it cannot guarantee the number of required classes while estimating the histogram by Gaussian Mixture Models (GMM). in this paper, to overcome these shortages, a novel thresholding approach by combining EM and Salp Swarm Algorithm (SSA) is developed. SSA suggests potential points to the EM algorithm to fly to a better position. Moreover, a new mechanism is considered to maintain the number of desired clusters. Twenty-four medical test images are selected and examined by standard metrics such as PSNR and FSIM. The proposed method is compared with the traditional EM algorithm, and an average improvement of 5.27% in PSNR values and 2.01% in FSIM values were recorded. Also, the proposed approach is compared with four existing segmentation techniques by using CT scan images that Qatar University has collected. Experimental results depict that the proposed method obtains the first rank in terms of PSNR and the second rank in terms of FSIM. It has been observed that the proposed technique performs better performance in the segmentation result compared to other considered state-of-the-art methods.

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The Bonferroni mean-type pre-aggregation operators construction and generalization: Application to edge detection

Cited by 19 publications

References 38 publications

Generalized fractal dimensions based comparison analysis of edge detection methods in CT images for estimating the infection of COVID-19 disease

Generalized fractal dimensions based comparison analysis of edge detection methods in CT images for estimating the infection of COVID-19 disease

Systematic Review of Aggregation Functions Applied to Image Edge Detection

An efficient image segmentation method based on expectation maximization and Salp swarm algorithm

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