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Edge Detection of COVID-19 CT Image Based on GF_SSR, Improved Multiscale Morphology, and adaptive threshold

Computer Modeling in Engineering & Sciences 2022年第7期132卷 81-94页

作者： Shouming Hou Chaolan Jia Kai Li Liya Fan Jincheng Guo Mackenzie Brown School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China City College Xi’an Jiaotong UniversityXi’an710018China Department of Thoracic Surgery Jiaozuo Second People’s HospitalJiaozuo454000China School of Engineering Edith Cowan UniversityJoondalup6027Australia

Edge detection is an effective method for image segmentation and feature extraction.Therefore,extracting weak edges with the inhomogeneous gray of Corona Virus Disease 2019(COVID-19)CT images is extremely important.Multiscale morphology has been widely used in the edge detection of medical images due to its excellent boundary detection accuracy.In this paper,we propose a weak edge detection method based on Gaussian filtering and singlescale Retinex(GF_SSR),and improved multiscale morphology and adaptive threshold binarization(IMSM_ATB).As all the CT images have noise,we propose to remove image noise by Gaussian filtering.The edge of CT images is enhanced using the SSR algorithm.In addition,based on the extracted edge of CT images using improved Multiscale morphology,a particle swarm optimization(PSO)algorithm is introduced to binarize the image by automatically getting the optimal threshold.To evaluate our method,we use images from three datasets,namely COVID-19,Kaggle-COVID-19,and COVID-Chestxray,respectively.The average values of results are worthy of reference,with the Shannon information entropy of 1.8539,the Precision of 0.9992,the Recall of 0.8224,the F-Score of 1.9158,running time of 11.3000.Finally,three types of lesion images in the COVID-19 dataset are selected to evaluate the visual effects of the proposed algorithm.Compared with the other four algorithms,the proposed algorithm effectively detects the weak edge of the lesion and provides help for image segmentation and feature extraction.

关键词： COVID-19 SSR multiscale morphology PSO adaptive threshold edge detection

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Defocus Blur Segmentation Using Local Binary Patterns with adaptive threshold

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Computers, Materials & Continua 2022年第4期71卷 1597-1611页

作者： Usman Ali Muhammad Tariq Mahmood Future Convergence Engineering School of Computer Science and EngineeringKorea University of Technology and Education1600Chungjeol-roByeongcheon-myeonCheonan31253Korea

Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection and segmentation is a challenging task.Hence,the performance of the blur measure operator is an essential factor and needs improvement to attain perfection.In this paper,we propose an effective blur measure based on local binary pattern(LBP)with adaptive threshold for blur detection.The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur,that may not be suitable for images with variations in imaging conditions,blur amount and type.Contrarily,the proposed measure uses an adaptive threshold for each input image based on the image and blur properties to generate improved sharpness metric.The adaptive threshold is computed based on the model learned through support vector machine(SVM).The performance of the proposed method is evaluated using two different datasets and is compared with five state-of-the-art methods.Comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all of the compared methods.

关键词： adaptive threshold blur measure defocus blur segmentation local binary pattern support vector machine

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An automatic detection of green tide using multi-windows with their adaptive threshold from Landsat TM/ETM plus image

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Acta Oceanologica Sinica 2017年第11期36卷 106-114页

作者： WANG Changying CHU Jialan TAN Meng SHAO Fengjing SUI Yi LI Shujing School of Data Science and Software Engineering Qingdao UniversityQingdao 266071China Institute of Big Data Technology and Smart City of Qingdao Qingdao 266071China Key laboratory of Marine Red Tide Disaster Three-dimensional Monitoring Technology and Application East China Sea BranchState Oceanic AdministrationShanghai 200080China National Marine Environmental Monitoring Center State Oceanic AdministrationDalian 116023China North China Sea Data and Information Service Center North China Sea BranchState Oceanic AdministrationQingdao 266061China

Since the atmospheric correction is a necessary preprocessing step of remote sensing image before detecting green tide, the introduced error directly affects the detection precision. Therefore, the detection method of green tide is presented from Landsat TM/ETM plus image which needs not the atmospheric correction. In order to achieve an automatic detection of green tide, a linear relationship（y =0.723 x＋0.504） between detection threshold y and subtraction x（x=λnir–λred） is found from the comparing Landsat TM/ETM plus image with the field surveys.Using this relationship, green tide patches can be detected automatically from Landsat TM/ETM plus image.Considering there is brightness difference between different regions in an image, the image will be divided into a plurality of windows（sub-images） with a same size firstly, and then each window will be detected using an adaptive detection threshold determined according to the discovered linear relationship. It is found that big errors will appear in some windows, such as those covered by clouds seriously. To solve this problem, the moving step k of windows is proposed to be less than the window width n. Using this mechanism, most pixels will be detected[n/k]×[n/k] times except the boundary pixels, then every pixel will be assigned the final class（green tide or sea water） according to majority rule voting strategy. It can be seen from the experiments, the proposed detection method using multi-windows and their adaptive thresholds can detect green tide from Landsat TM/ETM plus image automatically. Meanwhile, it avoids the reliance on the accurate atmospheric correction.

关键词： automatic detection green tide adaptive threshold Landsat TM/ETM plus image

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TRANSLATION-INVARIANT BASED adaptive threshold DENOISING FOR IMPACT SIGNAL

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Chinese Journal of Mechanical Engineering 2004年第4期17卷 552-555页

作者： GaiGuanghong QuLiangsheng SchoolofMechanicalEngineering Xi＇anJiaotongUniversityXi＇an710049China

A translation-invariant based adaptive threshold denoising method formechanical impact signal is proposed. Compared with traditional wavelet denoising methods, itsuppresses pseudo-Gibbs phenomena in the neighborhood of signal discontinuities. To remedy thedrawbacks of conventional threshold functions, a new improved threshold function is introduced. Itpossesses more advantages than others. Moreover, based on utilizing characteristics of signal, aadaptive threshold selection procedure for impact signal is proposed. It is data-driven andlevel-dependent, therefore, it is more rational than other threshold estimation methods. Theproposed method is compared to alternative existing methods, and its superiority is revealed bysimulation and real data examples.

关键词： Translation-invariant adaptive threshold Impact signal Denoising Wavelettransform

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QRS waves detection algorithm based on positive-negative adaptive threshold method

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Journal of Beijing Institute of Technology 2014年第1期23卷 63-66页

作者：尚宇雷莎莎 School of Electronic Information Engineering Xi’an Technological University

In order to accurately detect the occasional negative R waves in electrocardiography （ECG） signals, the positive-negative adaptive threshold method is adopted to determine the positive R waves and the negative R waves, according to difference characteristics of ECG signals. The Q and S waves can then be accurately positioned based on the basic characteristics of QRS waves. Finally, the algorithm simulation is made based on the signals from MIT-BIH database with MATLAB. The ex- perimental results show that the algorithm can improve the detection accuracy rate to 99. 91% and o- vercome the problem of larger computation load for wavelet transform and other methods, so the al- gorithm is suitable for real-time detection.

关键词： QRS wave detection adaptive threshold diference electrocardiography（ECG） signals

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adaptive threshold Estimation of Open Set Voiceprint Recognition Based on OTSU and Deep Learning

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Journal of Applied Mathematics and Physics 2020年第11期8卷 2671-2682页

作者： Xudong Li Xinjia Yang Linhua Zhou School of Science Changchun University of Science and Technology Changchun China Provincial Demonstration Center for Experimental Mathematics Education (Changchun University of Science and Technology) Changchun China

Aiming at the problem of open set voiceprint recognition, this paper proposes an adaptive threshold algorithm based on OTSU and deep learning. The bottleneck technology of open set voiceprint recognition lies in the calculation of similarity values and thresholds of speakers inside and outside the set. This paper combines deep learning and machine learning methods, and uses a Deep Belief Network stacked with three layers of Restricted Boltzmann Machines to extract deep voice features from basic acoustic features. And by training the Gaussian Mixture Model, this paper calculates the similarity value of the feature, and further determines the threshold of the similarity value of the feature through OTSU. After experimental testing, the algorithm in this paper has a false rejection rate of 3.00% for specific speakers, a false acceptance rate of 0.35% for internal speakers, and a false acceptance rate of 0 for external speakers. This improves the accuracy of traditional methods in open set voiceprint recognition. This proves that the method is feasible and good recognition effect.

关键词： Voiceprint Recognition Deep Neural Network (DNN) OTSU adaptive threshold

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Image Segmentation Based on Block Level and Hybrid Directional Local Extrema

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Computers, Materials & Continua 2022年第2期70卷 3939-3954页

作者： Ghanshyam Raghuwanshi Yogesh Gupta Deepak Sinwar Dilbag Singh Usman Tariq Muhammad Attique Kuntha Pin Yunyoung Nam Manipal University Jaipur Jaipur302026India BMLMunjal University GurugramIndia Bennett University Greater NoidaIndia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science HITEC University TaxilaTaxilaPakistan Department of ICT Convergence Soonchunhyang UniversityAsan 31538Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

In the recent decade,the digitalization of various tasks has added great flexibility to human lifestyle and has changed daily routine activities of communities.Image segmentation is a key step in digitalization.Segmentation plays a key role in almost all areas of image processing,and various approaches have been proposed for image segmentation.In this paper,a novel approach is proposed for image segmentation using a nonuniform adaptive strategy.Region-based image segmentation along with a directional binary pattern generated a better segmented image.An adaptive mask of 8×8 was circulated over the pixels whose bit value was 1 in the generated directional binary pattern.Segmentation was performed in three phases:first,an image was divided into sub-images or image chunks;next,the image patches were taken as input,and an adaptive threshold was generated;and finally the image chunks were processed separately by convolving the adaptive mask on the image chunks.Gradient and Laplacian of Gaussian algorithms along with directional extrema patterns provided a double check for boundary pixels.The proposed approach was tested on chunks of varying sizes,and after multiple iterations,it was found that a block size of 8×8 performs better than other chunks or block sizes.The accuracy of the segmentation technique was measured in terms of the count of ill regions,which were extracted after the segmentation process.

关键词： Image segmentation HDEP block-level processing adaptive threshold

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A Sea Ice Recognition Algorithm in Bohai Based on Random Forest

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Computers, Materials & Continua 2022年第11期73卷 3721-3739页

作者： Tao Li Di Wu Rui Han Jinyue Xia Yongjun Ren School of Artificial Intelligence/School of Future Technology Nanjing University of Information Science and TechnologyNanjing210044China Unit 93117 of PLA PLAJiangsu210000China International Business Machines Corporation(IBM) NY100014USA School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China

As an important maritime hub,Bohai Sea Bay provides great convenience for shipping and suffers from sea ice disasters of different severity every winter,which greatly affects the socio-economic and development of the region.Therefore,this paper uses FY-4A(a weather satellite)data to study sea ice in the Bohai Sea.After processing the data for land removal and cloud detection,it combines multi-channel threshold method and adaptive threshold algorithm to realize the recognition of Bohai Sea ice under clear sky conditions.The random forests classification algorithm is introduced in sea ice identification,which can achieve a certain effect of sea ice classification recognition under cloud cover.Under non-clear sky conditions,the results of Bohai Sea ice identification based on random forests have been improved,and the algorithm can effectively identify Bohai Sea Ice and can improve the accuracy of sea ice identification,which lays a foundation for the accuracy and stability of sea ice identification.It realizes sea ice identification in the Bohai Sea and provides data support and algorithm support for marine climate forecasting related departments.

关键词： FY-4A random forests Bohai Sea Ice sea ice identification adaptive threshold

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A Novel Segment White Matter Hyperintensities Approach for Detecting Alzheimer

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Computer Systems Science & Engineering 2023年第3期44卷 2715-2726页

作者： Antonitta Eileen Pious U.K.Sridevi Department of Science and Humanities Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Applied Mathematics and Computational Sciences PSG College of TechnologyCoimbatore641008India

Segmentation has been an effective step that needs to be done before the classification or detection of an anomaly like Alzheimer’s on a brain scan.Segmentation helps detect pixels of the same intensity or volume and group them together as one class or region,where in that particular region of interest(ROI)can be concentrated on,rather than focusing on the entire image.In this paper White Matter Hyperintensities(WMH)is taken as a strong biomarker that supports and determines the presence of Alzheimer’s.As thefirst step a proper segmentation of the lesions has to be carried out.As pointed out in various other research papers,when the WMH area is very small or in places like the Septum Pellucidum the detection of the lesion is hard tofind.To overcome such problem areas a very optimized and accurate threshold would be required to have a precise segmentation to detect the area of localization.This would help in proper detection and classification of the Anomaly.In this paper an elaborate comparison of various thresholding techniques has been done for segmentation.A novel idea for detection of Alzheimer’s has been presented in this paper,which encompasses the effectiveness of an optimized and adaptive technique.The Unet architecture has been taken as the baseline model with an adaptive kernel model embedded within the architecture.Various state-of-the-art technologies have been used with the dataset and a comparative study has been presented with the current architecture used in the paper.The lesion segmentation in narrow areas has accurately been detected compared to the other models and the number of false positives has been reduced to a great extent.

关键词： Alzheimer’s adaptive threshold deep learning segmentation

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An adaptive threshold edge detection method based on the law of gravity

An adaptive threshold edge detection method based on the law...

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第25届中国控制与决策会议

作者： zhonghai li zhihui yang wenlong wang jianguo cui College of Automation Shenyang Aerospace University

ISBN: (纸本)9781467355339

This paper put forward an edge detection algorithm which based on the law of gravity adaptive threshold. This algorithm is not only has good effect for common image edge detection, but also for the noise of the image edge detection and good results have been achieved. According to the law of universal gravitation of physics, the author designs a kind of template which applied to edge detection. Then, designs a kind of dynamic adaptive threshold method can be used for edge detection because that a human visual system in light is better than the dark area have small noise sensitivity. This algorithm make up for the traditional algorithm in the whole image only used a single threshold. Through the experimental comparison, this paper algorithm can create dynamic adaptive threshold value. The algorithm has good inhibition effect used in image with salt and pepper noise and gaussian noise. it can extract image edge efficiently.

关键词： image processing edge detection adaptive threshold algorithm template

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