发布求助
文献互助 智能选刊 最新文献

Medical Data Security for Bioengineers最新文献

筛选
英文 中文
Implementation and Performance Assessment of Biomedical Image Compression and Reconstruction Algorithms for Telemedicine Applications 远程医疗应用中生物医学图像压缩与重建算法的实现与性能评估
Medical Data Security for Bioengineers Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7952-6.CH003
C. Bhardwaj, Urvashi Sharma, Shruti Jain, M. Sood
{"title":"Implementation and Performance Assessment of Biomedical Image Compression and Reconstruction Algorithms for Telemedicine Applications","authors":"C. Bhardwaj, Urvashi Sharma, Shruti Jain, M. Sood","doi":"10.4018/978-1-5225-7952-6.CH003","DOIUrl":"https://doi.org/10.4018/978-1-5225-7952-6.CH003","url":null,"abstract":"Compression serves as a significant feature for efficient storage and transmission of medical, satellite, and natural images. Transmission speed is a key challenge in transmitting a large amount of data especially for magnetic resonance imaging and computed tomography scan images. Compressive sensing is an optimization-based option to acquire sparse signal using sub-Nyquist criteria exploiting only the signal of interest. This chapter explores compressive sensing for correct sensing, acquisition, and reconstruction of clinical images. In this chapter, distinctive overall performance metrics like peak signal to noise ratio, root mean square error, structural similarity index, compression ratio, etc. are assessed for medical image evaluation by utilizing best three reconstruction algorithms: basic pursuit, least square, and orthogonal matching pursuit. Basic pursuit establishes a well-renowned reconstruction method among the examined recovery techniques. At distinct measurement samples, on increasing the number of measurement samples, PSNR increases significantly and RMSE decreases.","PeriodicalId":416673,"journal":{"name":"Medical Data Security for Bioengineers","volume":"296 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115540429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Electrocardiogram Beat Classification Using BAT-Optimized Fuzzy KNN Classifier 基于蝙蝠优化模糊KNN分类器的心电图心率分类
Medical Data Security for Bioengineers Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7952-6.CH007
A. Verma, I. Saini, B. Saini
{"title":"Electrocardiogram Beat Classification Using BAT-Optimized Fuzzy KNN Classifier","authors":"A. Verma, I. Saini, B. Saini","doi":"10.4018/978-1-5225-7952-6.CH007","DOIUrl":"https://doi.org/10.4018/978-1-5225-7952-6.CH007","url":null,"abstract":"In this chapter, the BAT-optimized fuzzy k-nearest neighbor (FKNN-BAT) algorithm is proposed for discrimination of the electrocardiogram (ECG) beats. The five types of beats (i.e., normal [N], right bundle block branch [RBBB], left bundle block branch [LBBB], atrial premature contraction [APC], and premature ventricular contraction [PVC]) are taken from MIT-BIH arrhythmia database for the experimentation. Thereafter, the features are extracted from five type of beats and fed to the proposed BAT-tuned fuzzy KNN classifier. The proposed classifier achieves the overall accuracy of 99.88%.","PeriodicalId":416673,"journal":{"name":"Medical Data Security for Bioengineers","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115554117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Optimization Techniques for the Multilevel Thresholding of the Medical Images 医学图像多层次阈值分割优化技术
Medical Data Security for Bioengineers Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7952-6.CH009
T. Kaur, B. Saini, Savita Gupta
{"title":"Optimization Techniques for the Multilevel Thresholding of the Medical Images","authors":"T. Kaur, B. Saini, Savita Gupta","doi":"10.4018/978-1-5225-7952-6.CH009","DOIUrl":"https://doi.org/10.4018/978-1-5225-7952-6.CH009","url":null,"abstract":"Multilevel thresholding is segmenting the image into several distinct regions. Medical data like magnetic resonance images (MRI) contain important clinical information that is crucial for diagnosis. Hence, automatic segregation of tissue constituents is of key interest to clinician. In the chapter, standard entropies (i.e., Kapur and Tsallis) are explored for thresholding of brain MR images. The optimal thresholds are obtained by the maximization of these entropies using the particle swarm optimization (PSO) and the BAT optimization approach. The techniques are implemented for the segregation of various tissue constituents (i.e., cerebral spinal fluid [CSF], white matter [WM], and gray matter [GM]) from simulated images obtained from the brain web database. The efficacy of the thresholding technique is evaluated by the Dice coefficient (Dice). The results demonstrate that Tsallis' entropy is superior to the Kapur's entropy for the segmentation CSF and WM. Moreover, entropy maximization using BAT algorithm attains a higher Dice in contrast to PSO.","PeriodicalId":416673,"journal":{"name":"Medical Data Security for Bioengineers","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127821104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Bernoulli's Chaotic Map-Based 2D ECG Image Steganography 基于Bernoulli混沌映射的二维心电图像隐写
Medical Data Security for Bioengineers Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7952-6.CH011
Anukul Pandey, B. Saini, B. P. Singh, Neetu Sood
{"title":"Bernoulli's Chaotic Map-Based 2D ECG Image Steganography","authors":"Anukul Pandey, B. Saini, B. P. Singh, Neetu Sood","doi":"10.4018/978-1-5225-7952-6.CH011","DOIUrl":"https://doi.org/10.4018/978-1-5225-7952-6.CH011","url":null,"abstract":"Signal processing technology comprehends fundamental theory and implementations for processing data. The processed data is stored in different formats. The mechanism of electrocardiogram (ECG) steganography hides the secret information in the spatial or transformed domain. Patient information is embedded into the ECG signal without sacrificing the significant ECG signal quality. The chapter contributes to ECG steganography by investigating the Bernoulli's chaotic map for 2D ECG image steganography. The methodology adopted is 1) convert ECG signal into the 2D cover image, 2) the cover image is loaded to steganography encoder, and 3) secret key is shared with the steganography decoder. The proposed ECG steganography technique stores 1.5KB data inside ECG signal of 60 seconds at 360 samples/s, with percentage root mean square difference of less than 1%. This advanced 2D ECG steganography finds applications in real-world use which includes telemedicine or telecardiology.","PeriodicalId":416673,"journal":{"name":"Medical Data Security for Bioengineers","volume":"1245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129738932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
Robust Steganography in Non-QRS Regions of 2D ECG for Securing Patients' Confidential Information in E-Healthcare Paradigm 电子医疗模式下二维心电图非qrs区域的鲁棒隐写术保护患者机密信息
Medical Data Security for Bioengineers Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7952-6.CH002
Neetika Soni, I. Saini, B. P. Singh
{"title":"Robust Steganography in Non-QRS Regions of 2D ECG for Securing Patients' Confidential Information in E-Healthcare Paradigm","authors":"Neetika Soni, I. Saini, B. P. Singh","doi":"10.4018/978-1-5225-7952-6.CH002","DOIUrl":"https://doi.org/10.4018/978-1-5225-7952-6.CH002","url":null,"abstract":"The upsurge in the communication infrastructure and development in internet of things (IoT) has promoted e-healthcare services to provide remote assistance to homebound patients. It, however, increases the demand to protect the confidential information from intentional and unintentional access by unauthorized persons. This chapter is focused on steganography-based data hiding technique for ECG signal in which the selected ECG samples of non-QRS region are explored to embed the secret information. An embedding site selection (ESS) algorithm is designed to find the optimum embedding locations. The performance of the method is evaluated on the basis of statistical parameters and clinically supportive measures. The efficiency is measured in terms of embedding capacity and BER while key space measures its robustness. The implementation has been tested on standard MIT-BIH arrhythmia database of 2 mins and 5 mins duration and found that the proposed technique embarks the proficiency to securely hide the secret information at minimal distortion.","PeriodicalId":416673,"journal":{"name":"Medical Data Security for Bioengineers","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115237416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Electrocardiogram Dynamic Interval Feature Extraction for Heartbeat Characterization 用于心跳表征的心电图动态间隔特征提取
Medical Data Security for Bioengineers Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7952-6.CH012
A. Verma, I. Saini, B. Saini
{"title":"Electrocardiogram Dynamic Interval Feature Extraction for Heartbeat Characterization","authors":"A. Verma, I. Saini, B. Saini","doi":"10.4018/978-1-5225-7952-6.CH012","DOIUrl":"https://doi.org/10.4018/978-1-5225-7952-6.CH012","url":null,"abstract":"In the chapter, dynamic time domain features are extracted in the proposed approach for the accurate classification of electrocardiogram (ECG) heartbeats. The dynamic time-domain information such as RR, pre-RR, post-RR, ratio of pre-post RR, and ratio of post-pre RR intervals to be extracted from the ECG beats in proposed approach for heartbeat classification. These four extracted features are combined and fed to k-nearest neighbor (k-NN) classifier with tenfold cross-validation to classify the six different heartbeats (i.e., normal [N], right bundle branch block [RBBB], left bundle branch block [LBBB], atrial premature beat [APC], paced beat [PB], and premature ventricular contraction[PVC]). The average sensitivity, specificity, positive predictivity along with overall accuracy is obtained as 99.77%, 99.97%, 99.71%, and 99.85%, respectively, for the proposed classification system. The experimental result tells that proposed classification approach has given better performance as compared with other state-of-the-art feature extraction methods for the heartbeat characterization.","PeriodicalId":416673,"journal":{"name":"Medical Data Security for Bioengineers","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127000504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Reliable Medical Image Communication in Healthcare IoT 医疗物联网中可靠的医疗图像通信
Medical Data Security for Bioengineers Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7952-6.CH001
S. Janakiraman, Sundararaman Rajagopalan, Rengarajan Amirtharajan
{"title":"Reliable Medical Image Communication in Healthcare IoT","authors":"S. Janakiraman, Sundararaman Rajagopalan, Rengarajan Amirtharajan","doi":"10.4018/978-1-5225-7952-6.CH001","DOIUrl":"https://doi.org/10.4018/978-1-5225-7952-6.CH001","url":null,"abstract":"Images have been widely used in the medical field for various diagnostic purposes. In the field of healthcare IoT, secure communication of a medical image concerned with an individual is a crucial task. Embedding patients' personal information as an invisible watermark in their medical images helps to authenticate the ownership identification process. Reliable communication of medical image can be thereby ensured concerning authentication and integrity. Images in DICOM format with a pixel resolution of 8-bit depth are used for medical diagnostics. This chapter deals about the development of a lightweight algorithm to insert patients' identities as an invisible watermark in random edge pixels of DICOM images. This chapter describes the implementation of the proposed lightweight watermarking algorithm on a RISC microcontroller suitable for healthcare IoT applications. Imperceptibility level of the watermarked medical image was analyzed besides its lightweight performance validation on the constrained IoT platform.","PeriodicalId":416673,"journal":{"name":"Medical Data Security for Bioengineers","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133305010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Healthcare Informatics Using Modern Image Processing Approaches 使用现代图像处理方法的医疗信息学
Medical Data Security for Bioengineers Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7952-6.CH013
R. Kashyap, Surendra Rahamatkar
{"title":"Healthcare Informatics Using Modern Image Processing Approaches","authors":"R. Kashyap, Surendra Rahamatkar","doi":"10.4018/978-1-5225-7952-6.CH013","DOIUrl":"https://doi.org/10.4018/978-1-5225-7952-6.CH013","url":null,"abstract":"Medical image segmentation is the first venture for abnormal state image analysis, significantly lessening the multifaceted nature of substance investigation of pictures. The local region-based active contour may have a few burdens. Segmentation comes about to intensely rely on the underlying shape choice which is an exceptionally capable errand. In a few circumstances, manual collaborations are infeasible. To defeat these deficiencies, the proposed method for unsupervised segmentation of viewer's consideration object of medical images given the technique with the help of the shading boosting Harris finder and the center saliency map. Investigated distinctive techniques to consider the image data and present a formerly utilized energy-based active contour method dependent on the choice of high certainty forecasts to allocate pseudo-names consequently with the point of diminishing the manual explanations.","PeriodicalId":416673,"journal":{"name":"Medical Data Security for Bioengineers","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123131112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Advancements in Data Security and Privacy Techniques Used in IoT-Based Hospital Applications 基于物联网的医院应用中数据安全和隐私技术的进展
Medical Data Security for Bioengineers Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7952-6.CH010
Ankita Tiwari, R. P. Tripathi, D. Bhatia
{"title":"Advancements in Data Security and Privacy Techniques Used in IoT-Based Hospital Applications","authors":"Ankita Tiwari, R. P. Tripathi, D. Bhatia","doi":"10.4018/978-1-5225-7952-6.CH010","DOIUrl":"https://doi.org/10.4018/978-1-5225-7952-6.CH010","url":null,"abstract":"The risk of encountering new diseases is on the rise in medical centers globally. By employing advancements in medical sensors technology, new health monitoring programs are being developed for continuous monitoring of physiological parameters in patients. Since the stored medical data is personal health record of an individual, it requires delicate and secure handling. In wireless transmission networks, medical data is disposed of to avoid loss due to alteration, eavesdropping, etc. Hence, privacy and security of the medical data are the major considerations during wireless transfer through Medical Sensor Network of MSNs. This chapter delves upon understanding the working of a secure monitoring system wherein the data could be continuously observed with the support of MSNs. Process of sanctioning secure data to authorized users such as physician, clinician, or patient through the key provided to access the file are also explained. Comparative analysis of the encryption techniques such as paillier, RSA, and ELGamal has been included to make the reader aware in selecting a useful technique for a particular hospital application.","PeriodicalId":416673,"journal":{"name":"Medical Data Security for Bioengineers","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132645717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信