Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-2795-5.ch002
Randhir Kumar, Rakesh Tripathi
Currently, sharing and access of medical imaging is a significant element of present healthcare systems, but the existing infrastructure of medical image sharing depends on third-party approval. In this chapter, the authors have proposed a framework in order to provide a decentralized storage model for medical image sharing through IPFS and blockchain technology that remove the hurdle of third-party dependency. In the proposed model, the authors are sharing the imaging and communications in medicine (DICOM) medical images, which consist of various information related to disease, and hence, the framework can be utilized in the real-time application of the healthcare system. Moreover, the framework maintains the feature of immutability, privacy, and availability of information owing to the blockchain-based decentralized storage model. Furthermore, the authors have also discussed how the information can be accessed by the peers in the blockchain network with the help of consensus. To implement the framework, they have used the python ask and anaconda python.
{"title":"Building an IPFS and Blockchain-Based Decentralized Storage Model for Medical Imaging","authors":"Randhir Kumar, Rakesh Tripathi","doi":"10.4018/978-1-7998-2795-5.ch002","DOIUrl":"https://doi.org/10.4018/978-1-7998-2795-5.ch002","url":null,"abstract":"Currently, sharing and access of medical imaging is a significant element of present healthcare systems, but the existing infrastructure of medical image sharing depends on third-party approval. In this chapter, the authors have proposed a framework in order to provide a decentralized storage model for medical image sharing through IPFS and blockchain technology that remove the hurdle of third-party dependency. In the proposed model, the authors are sharing the imaging and communications in medicine (DICOM) medical images, which consist of various information related to disease, and hence, the framework can be utilized in the real-time application of the healthcare system. Moreover, the framework maintains the feature of immutability, privacy, and availability of information owing to the blockchain-based decentralized storage model. Furthermore, the authors have also discussed how the information can be accessed by the peers in the blockchain network with the help of consensus. To implement the framework, they have used the python ask and anaconda python.","PeriodicalId":206521,"journal":{"name":"Advancements in Security and Privacy Initiatives for Multimedia Images","volume":"45 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":"133299251","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}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-2795-5.ch005
M. S. Velpuru
Digital content security gained immense attention over past two decades due rapid digitization of industries and government sectors, and providing security to digital content became a vital challenge. Digital watermarking is one prominent solution to protect digital content from tamper detection and content authentication. However, digital watermarking can alter sensitive information present on cover-content during embedding, then the recovery of exact cover-content may not be possible during extraction process. Moreover, certain applications may not allow small distortions in cover-content. Hence, reversible watermarking techniques of digital content can extract cover-content and watermark completely. Additionally, reversible watermarking is gaining popularity by an increasing number of applications in military, law enforcement, healthcare. In this chapter, the authors compare and contrast the different reversible watermarking techniques with quality and embedding capacity parameters. This survey is essential due to the rapid evolution of reversible watermarking techniques.
{"title":"Reversible Watermarking Techniques","authors":"M. S. Velpuru","doi":"10.4018/978-1-7998-2795-5.ch005","DOIUrl":"https://doi.org/10.4018/978-1-7998-2795-5.ch005","url":null,"abstract":"Digital content security gained immense attention over past two decades due rapid digitization of industries and government sectors, and providing security to digital content became a vital challenge. Digital watermarking is one prominent solution to protect digital content from tamper detection and content authentication. However, digital watermarking can alter sensitive information present on cover-content during embedding, then the recovery of exact cover-content may not be possible during extraction process. Moreover, certain applications may not allow small distortions in cover-content. Hence, reversible watermarking techniques of digital content can extract cover-content and watermark completely. Additionally, reversible watermarking is gaining popularity by an increasing number of applications in military, law enforcement, healthcare. In this chapter, the authors compare and contrast the different reversible watermarking techniques with quality and embedding capacity parameters. This survey is essential due to the rapid evolution of reversible watermarking techniques.","PeriodicalId":206521,"journal":{"name":"Advancements in Security and Privacy Initiatives for Multimedia Images","volume":"15 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":"134549692","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}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-2795-5.ch007
Neetu Faujdar, Anant Joshi
With massive advancements in the fields of data analysis and data mining, a new importance has been gained by data visualization. Data visualization focuses on visualizing and abstracting complex data to make it comprehensible and easy to understand using visual representation of information. Analysis of crime and crime-related data has been steadily popularizing over the last decade, and this chapter aims at visualizing such data. Crime data for several different types of crime for many countries in the world has been collected, compiled, processed, analyzed, and visualized in this chapter. Predictive analysis of this data has also been performed using time series analysis. This chapter aims to create a hub where internet users can easily view and interpret this data.
{"title":"Time Series Analysis for Crime Forecasting Using ARIMA (Autoregressive Integrated Moving Average) Model","authors":"Neetu Faujdar, Anant Joshi","doi":"10.4018/978-1-7998-2795-5.ch007","DOIUrl":"https://doi.org/10.4018/978-1-7998-2795-5.ch007","url":null,"abstract":"With massive advancements in the fields of data analysis and data mining, a new importance has been gained by data visualization. Data visualization focuses on visualizing and abstracting complex data to make it comprehensible and easy to understand using visual representation of information. Analysis of crime and crime-related data has been steadily popularizing over the last decade, and this chapter aims at visualizing such data. Crime data for several different types of crime for many countries in the world has been collected, compiled, processed, analyzed, and visualized in this chapter. Predictive analysis of this data has also been performed using time series analysis. This chapter aims to create a hub where internet users can easily view and interpret this data.","PeriodicalId":206521,"journal":{"name":"Advancements in Security and Privacy Initiatives for Multimedia Images","volume":"224 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":"133079396","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}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-2795-5.ch003
Y. Gupta
Big data refers to the massive amount of data from sundry sources (gregarious media, healthcare, different sensor, etc.) with very high velocity. Due to expeditious growth, the multimedia or image data has rapidly incremented due to the expansion of convivial networking, surveillance cameras, satellite images, and medical images. Healthcare is the most promising area where big data can be applied to make a vicissitude in human life. The process for analyzing the intricate data is mundanely concerned with the disclosing of hidden patterns. In healthcare fields capturing the visual context of any medical images, extraction is a well introduced word in digital image processing. The motive of this research is to present a detailed overview of big data in healthcare and processing of non-invasive medical images with the avail of feature extraction techniques such as region growing segmentation, GLCM, and discrete wavelet transform.
{"title":"Evolution of Big Data in Medical Imaging Modalities to Extract Features Using Region Growing Segmentation, GLCM, and Discrete Wavelet Transform","authors":"Y. Gupta","doi":"10.4018/978-1-7998-2795-5.ch003","DOIUrl":"https://doi.org/10.4018/978-1-7998-2795-5.ch003","url":null,"abstract":"Big data refers to the massive amount of data from sundry sources (gregarious media, healthcare, different sensor, etc.) with very high velocity. Due to expeditious growth, the multimedia or image data has rapidly incremented due to the expansion of convivial networking, surveillance cameras, satellite images, and medical images. Healthcare is the most promising area where big data can be applied to make a vicissitude in human life. The process for analyzing the intricate data is mundanely concerned with the disclosing of hidden patterns. In healthcare fields capturing the visual context of any medical images, extraction is a well introduced word in digital image processing. The motive of this research is to present a detailed overview of big data in healthcare and processing of non-invasive medical images with the avail of feature extraction techniques such as region growing segmentation, GLCM, and discrete wavelet transform.","PeriodicalId":206521,"journal":{"name":"Advancements in Security and Privacy Initiatives for Multimedia Images","volume":"200 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":"123022658","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}
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