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Detection of Osteoarthritis Based on EHO thresholding

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Computers, Materials & Continua 2022年第6期71卷 5783-5798页

作者： R.Kanthavel R.Dhaya Kanagaraj Venusamy Department of Computer Engineering King Khalid UniversityAbhaSaudi Arabia Department of Computer Science King Khalid UniversitySarat Abidha CampusAbhaSaudi Arabia Department of Engineering University of Technology and Applied SciencesAl MussanahSultanate of Oman

Knee Osteoarthritis(OA)is a joint disease that is commonly observed in people around the world.Osteoarthritis commonly affects patients who are obese and those above the age of 60.A valid knee image was generated by Computed Tomography(CT).In this work,efficient segmentation of CT images using Elephant Herding Optimization(EHO)optimization is implemented.The initial stage employs,the CT image normalization and the normalized image is incited to image enhancement through histogram correlation.Consequently,the enhanced image is segmented by utilizing Niblack and Bernsen algorithm.The(EHO)optimized outcome is evaluated in two steps.The initial step includes image enhancement with the measure of Mean square error(MSE),Peak signal to noise ratio(PSNR)and Structural similarity index(SSIM).The following step includes the segmentation which includes the measure ofAccuracy,Sensitivity and Specificity.The comparative analysis of EHO provides 95%of accuracy,94%of specificity and 93%of sensitivity than that of Active contour and Otsu threshold.

关键词： Osteoarthritis CT images correlation histogram thresholding Niblack&Bernsen algorithm EH optimization

An Iterative thresholding Method for the Minimum Compliance Problem

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Communications in Computational Physics 2023年第4期33卷 1189-1216页

作者： Luyu Cen Wei Hu Dong Wang Xiaoping Wang Department of Mathematics Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina The Program in Applied and Computational Mathematics Princeton UniversityPrincetonNew JerseyUSA School of Science and Engineering The Chinese University of Hong KongShenzhenGuangdong 518172China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big DataGuangdong 518172China

In this paper,we propose a simple energy decaying iterative thresholding algorithm to solve the two-phase minimum compliance problem.The material domain is implicitly represented by its characteristic function,and the problem is formulated into a minimization problem by the principle of minimum complementary energy.We prove that the energy is decreasing in each iteration.Two effective continuation schemes are proposed to avoid trapping into the local minimum.Numerical results on 2D isotropic linear material demonstrate the effectiveness of the proposed methods.

关键词： Minimum compliance convolution thresholding linear elasticity

Alphabet-Level Indian Sign Language Translation to Text Using Hybrid-AO thresholding with CNN

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Intelligent Automation & Soft Computing 2023年第9期37卷 2567-2582页

作者： Seema Sabharwal Priti Singla Department of Computer Science and Engineering Baba Mastnath UniversityRohtak124001India Department of Computer Science Government Post Graduate College for WomenPanchkula134109India

Sign language is used as a communication medium in the field of trade,defence,and in deaf-mute communities worldwide.Over the last few decades,research in the domain of translation of sign language has grown and become more challenging.This necessitates the development of a Sign Language Translation System(SLTS)to provide effective communication in different research domains.In this paper,novel Hybrid Adaptive Gaussian thresholding with Otsu Algorithm(Hybrid-AO)for image segmentation is proposed for the translation of alphabet-level Indian Sign Language(ISLTS)with a 5-layer Convolution Neural Network(CNN).The focus of this paper is to analyze various image segmentation(Canny Edge Detection,Simple thresholding,and Hybrid-AO),pooling approaches(Max,Average,and Global Average Pooling),and activation functions(ReLU,Leaky ReLU,and ELU).5-layer CNN with Max pooling,Leaky ReLU activation function,and Hybrid-AO(5MXLR-HAO)have outperformed other frameworks.An open-access dataset of ISL alphabets with approx.31 K images of 26 classes have been used to train and test the model.The proposed framework has been developed for translating alphabet-level Indian Sign Language into text.The proposed framework attains 98.95%training accuracy,98.05%validation accuracy,and 0.0721 training loss and 0.1021 validation loss and the perfor-mance of the proposed system outperforms other existing systems.

关键词： Sign language translation CNN thresholding Indian sign language

A Squared-Chebyshev wavelet thresholding based 1D signal compression

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Defence Technology（防务技术） 2019年第3期15卷 426-431页

作者： Hanan A.R. Akkar Wael A.H. Hadi Ibraheem H. Al-Dosari Electronic Engineering in University of Technology Iraq Communication Engineering in University of Technology Iraq Electronic Engineering at Al-Rafidain University College Iraq

In this paper a square wavelet thresholding method is proposed and evaluated as compared to the other classical wavelet thresholding methods (like soft and hard). The main advantage of this work is to design and implement a new wavelet thresholding method and evaluate it against other classical wavelet thresholding methods and hence search for the optimal wavelet mother function among the wide families with a suitable level of decomposition and followed by a novel thresholding method among the existing methods. This optimized method will be used to shrink the wavelet coefficients and yield an adequate compressed pressure signal prior to transmit it. While a comparison evaluation analysis is established, A new proposed procedure is used to compress a synthetic signal and obtain the optimal results through minimization the signal memory size and its transmission bandwidth. There are different performance indices to establish the comparison and evaluation process for signal compression;but the most well-known measuring scores are: NMSE, ESNR, and PDR. The obtained results showed the dominant of the square wavelet thresholding method against other methods using different measuring scores and hence the conclusion by the way for adopting this proposed novel wavelet thresholding method for 1D signal compression in future researches.

关键词： PDR (percentage root mean squared difference) RMSE (root mean square error) Signal compression Square wavelet thresholding

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Multilevel Image thresholding Using Tsallis Entropy and Cooperative Pigeon-inspired Optimization Bionic Algorithm

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Journal of Bionic Engineering 2019年第5期16卷 954-964页

作者： Yiin Wang Guangbin Zhang Xiaofeng Zhang School of Physics and Information Technology Shaanxi Normal UniversityXi'an 710119China

Multilevel thresholding is a simple and effective method in numerous image segmentation applications.In this paper,we propose a new multilevel thresholding method that uses cooperative pigeon-inspired optimization algorithm with dynamic distance threshold(CPIOD)for boosting applicability and the practicality of the optimum thresholding techniques.Firstly,we employ the cooperative be havior in the map and compass operator of the pigeon-inspired optimization algorithm to overcome the"curse of dimensionality"and help the algorithm converge fast.Then,a distance threshold is added to maintain the diversity of the pigeon population and increase the vitality to avoid local optimization.Tsallis entropy is used as the objective function to evaluate the optimum thresholds for the considered gray scale images.Four benchmark images are applied to test the property and the stability of the proposed CPIOD algorithm and three other optimization algorithms in multilevel thresholding problems.Segmentation results of four optimization algorithms show that CPIOD algorithm can not only get higher quality segmentation results,but also has better stability.

关键词： bionic algorithm multilevel thresholding Tsallis entropy pigeon-inspired optimization image segmentation

Fast recursive algorithm for two-dimensional Tsallis entropy thresholding method

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Journal of Systems Engineering and Electronics 2009年第3期20卷 619-624页

作者： Tang Yinggan Di Qiuyan Guan Xinping Inst. of Electrical Engineering Yanshan Univ. Qinhuangdao 066004 P. R. China

Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obstacle to real time image processing systems. A fast recursive algorithm for 2-D Tsallis entropy thresholding is proposed. The key variables involved in calculating 2-D Tsallis entropy are written in recursive form. Thus, many repeating calculations are avoided and the computation complexity reduces to O（L2） from O（L4）. The effectiveness of the proposed algorithm is illustrated by experimental results.

关键词： image segmentation thresholding Tsallis entropy fast recursive algorithm

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A Context Sensitive Multilevel thresholding Using Swarm Based Algorithms

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IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1471-1486页

作者： Shreya Pare Anil Kumar Varun Bajaj Girish Kumar Singh PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur 482005MPIndia Institute of Technology Roorkee Uttarakhand 247667India

In this paper, a comprehensive energy function is used to formulate the three most popular objective functions:Kapur's, Otsu and Tsalli's functions for performing effective multilevel color image thresholding. These new energy based objective criterions are further combined with the proficient search capability of swarm based algorithms to improve the efficiency and robustness. The proposed multilevel thresholding approach accurately determines the optimal threshold values by using generated energy curve, and acutely distinguishes different objects within the multi-channel complex images. The performance evaluation indices and experiments on different test images illustrate that Kapur's entropy aided with differential evolution and bacterial foraging optimization algorithm generates the most accurate and visually pleasing segmented images.

关键词： Color image segmentation Kapur's entropy multilevel thresholding Otsu method swarm based optimization algorithms Tsalli's entropy

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Automatic Delineation of Lung Parenchyma Based on Multilevel thresholding and Gaussian Mixture Modelling

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Computer Modeling in Engineering & Sciences 2018年第2期114卷 141-152页

作者： S.Gopalakrishnan A.Kandaswamy Department of Biomedical Engineering PSG College of TechnologyCoimbatoreTamil NaduIndia

Delineation of the lung parenchyma in the thoracic Computed Tomography(CT)is an important processing step for most of the pulmonary image analysis such as lung volume extraction,lung nodule detection and pulmonary vessel segmentation.An automatic method for accurate delineation of lung parenchyma in thoracic Computed Tomography images is presented in this paper.The proposed method involves a segmentation phase followed by a lung boundary correction technique.The tissues in the thoracic Computed Tomography can be represented by a number of Gaussians.We propose a histogram utilized Adaptive Multilevel thresholding(AMT)for estimating the total number of Gaussians and their initial parameters.The parameters of Gaussian components are updated by Expectation Maximization(EM)algorithm.The segmented lung parenchyma from the Gaussian Mixture model(GMM)undergoes an Adaptive Morphological Filtering(AMF)to reduce the boundary errors.The proposed method has been tested on 70 diseased and 119 normal lung images from 28 cases obtained from Lung Image Database Consortium(LIDC).The performance of the proposed system has been validated.

关键词： Lung parenchyma delineation thoracic computed tomography multilevel thresholding Gaussian mixture model Adaptive Morphological Filtering

Local thresholding with adaptive window shrinkage in the contourlet domain for image denoising

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Science China(Information Sciences) 2013年第9期56卷 65-73页

作者： SHEN XiaoHong WANG Kai GUO Qiang School of Computer Science and Technology Shandong University of Finance and Economics Shandong Provincial Key Laboratory of Digital Media Technology Shandong University of Finance and Economics

Threshold selection is a challenging job for the image denoising in the contourlet domain. In this paper, a new local threshold with adaptive window shrinkage is proposed. According to the anisotropic energy clusters in contourlet subbands, local adaptive elliptic windows are introduced to determine the neighboring coefficients with strong dependencies for each coefficient. Utilizing the maximum likelihood estimator within the adaptive window, the signal variance is estimated from the noisy neighboring coefficients. Based on the signal variance estimation, the new threshold is obtained in the Bayesian framework. Since it makes full use of the captured directional information of images, the threshold extends to the anisotropic spatial adaptability and behaves reliably. Simulation experiments show that the new method exhibits better performance than other outstanding wavelet and contourlet denoising schemes obviously, both in the PSNR value and the visual appearance.

关键词： image denoising thresholding contourlet transform adaptive window directional selectivity