Managing multiple medical image file formats and conventions

2003 ◽  
Author(s):  
Scott C. Neu ◽  
Daniel J. Valentino ◽  
Keith R. Ouellette ◽  
Arthur W. Toga
Keyword(s):  
Medical Image ◽  
Image File ◽  
File Formats

scholarly journals Medical Image File Formats

2013 ◽  
Vol 27 (2) ◽  
pp. 200-206 ◽  
Author(s):  
Michele Larobina ◽  
Loredana Murino
Keyword(s):  
Medical Image ◽  
Image File ◽  
File Formats

scholarly journals An Medical Image File Formats and Digital Image Conversion

2019 ◽  
Vol 9 (1S3) ◽  
pp. 74-78
Keyword(s):  
Digital Image ◽  
Medical Image ◽  
Imaging Informatics ◽  
Technology Initiative ◽  
Image File ◽  
Image Conversion ◽  
File Formats ◽  
Processing Step ◽  
Neuro Imaging ◽  
Image Format

Medical image file formats make the confusing aspect to young researchers who start work with medical images. Medical image format conversion is still a tedious task due to the different structure of files. Digital image conversion from the medical image is an important pre-processing step to the process and visualizes the data. This article presents an overview of the major medical image file formats such as Analyze, neuro-imaging informatics technology initiative (NIFTI), MINC, and digital imaging and communications in medicine (DICOM). Then the characteristics and strengths of the various formats are discussed. Also the article describes the easiest way of digital image conversion from medical image format.

scholarly journals Virtual Disks Performance Analysis Using Flexible I/O and Powerstat

2021 ◽  
Vol 9 (10) ◽  
pp. 267-296
Author(s):  
Pritam Patange
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Disk File ◽  
Virtual Machine Monitor ◽  
Image File ◽  
File Formats ◽  
Physical Infrastructure ◽  
Disk Image ◽  
The Impact

Abstract: Cloud computing has experienced significant growth in the recent years owing to the various advantages it provides such as 24/7 availability, quick provisioning of resources, easy scalability to name a few. Virtualization is the backbone of cloud computing. Virtual Machines (VMs) are created and executed by a software called Virtual Machine Monitor (VMM) or the hypervisor. It separates compute environments from the actual physical infrastructure. A disk image file representing a single virtual machine is created on the hypervisor’s file system. In this paper, we analysed the runtime performance of multiple different disk image file formats. The analysis comprises of four different parameters of performance namely- bandwidth, latency, input-output operations performed per second (IOPS) and power consumption. The impact of the hypervisor’s block and file sizes is also analysed for the different file formats. The paper aims to act as a reference for the reader in choosing the most appropriate disk file image format for their use case based on the performance comparisons made between different disk image file formats on two different hypervisors – KVM and VirtualBox. Keywords: Virtualization, Virtual disk formats, Cloud computing, fio, KVM, virt-manager, powerstat, VirtualBox.

Secure Data Hiding Using Eight Queens Solutions

2012 ◽  
Vol 6 (4) ◽  
pp. 55-70 ◽  
Author(s):  
Sunil Kumar Muttoo ◽  
Vinay Kumar ◽  
Abhishek Bansal
Keyword(s):  
Data Hiding ◽  
Cover Image ◽  
Image File ◽  
File Formats ◽  
Secure Data ◽  
Ascii Code

The 8-queens problem of placing 8 non-attacking queens on an 8x8 chessboard is used to hide message in an image. The method helps in randomizing the bit selection in a cover image for hiding purpose. Cover image is divided into blocks of 8x1 bytes and then masked with solutions of the 8-queens problem. Bits from the block are collected corresponding to the 8-queen solution to make a 7 bit string. LSB of the block is not considered. It gives a number in the range of 0 to 127. If a bit string, corresponding to the 8-queens solutions, matches with ASCII code of the first character from message, the corresponding solution number of the 8-queens problem is encrypted using RC4, and the cipher is stored in first block of the cover. This encrypted value works as key. The solution number corresponding to next character is XORED with the key and the resultant value is embedded in the LSB of next block. The algorithm has been tested with cover of different image file formats like BMP, PNG and TIFF. The algorithm provides very good capacity, imperceptibility and robustness.

scholarly journals Radtools: R utilities for smooth navigation of medical image data

2018 ◽  
Author(s):  
Pamela H Russell ◽  
Debashis Ghosh
Keyword(s):  
Medical Image ◽  
Image Data ◽  
R Package ◽  
Image Series ◽  
Link Type ◽  
File Formats ◽  
Medical Image Data ◽  
Image Metadata ◽  
The Individual ◽  
Radiological Image

AbstractThe radiology community has adopted several widely used standards for medical image files, including the popular DICOM (Digital Imaging and Communication in Medicine) and NIfTI (Neuroimaging Informatics Technology Initiative) standards. These file formats include image intensities as well as potentially extensive metadata. The NIfTI standard specifies a particular set of header fields describing the image and minimal information about the scan. DICOM headers can include any of over 4,000 available metadata attributes spanning a variety of topics. NIfTI files contain all slices for an image series, while DICOM files capture single slices and image series are typically organized into a directory. Each DICOM file contains metadata for the image series as well as the individual image slice.The programming environment R is popular for data analysis due to its free and open code, active ecosystem of tools and users, and excellent system of contributed packages. Currently, many published radiological image analyses are performed with proprietary software or custom unpublished scripts. However, R is increasing in popularity in this area due to several packages for processing and analysis of image files. While these R packages handle image import and processing, no existing package makes image metadata conveniently accessible. Extracting image metadata, combining across slices, and converting to useful formats can be prohibitively cumbersome, especially for DICOM files.We present radtools, an R package for smooth navigation of medical image data. Radtools makes the problem of extracting image metadata trivially simple, providing simple functions to explore and return information in familiar R data structures. Radtools also facilitates extraction of image data and viewing of image slices. The package is freely available under the MIT license at https://github.com/pamelarussell/radtools and is easily installable from the Comprehensive R Archive Network (https://cran.r-project.org/package=radtools).

scholarly journals A Streaming IO Base Class and Support for Streaming the MRC and VTK File Format

2010 ◽  
Author(s):  
Bradley Lowekamp ◽  
David Chen
Keyword(s):  
File Format ◽  
Image File ◽  
File Formats

This paper describes our contribution of three new classes to the Insight Toolkit community. We present a new ImageIO base class for streaming image file, along with two derived ImageIO classes for the VTK and the MRC file formats.

scholarly journals Quantitative Image Format for Electron Microscopy

2020 ◽  
Vol 26 (S2) ◽  
pp. 1176-1178
Author(s):  
Grigore Moldovan ◽  
Michael Zabel
Keyword(s):  
Electron Microscopy ◽  
Experimental Data ◽  
Quantitative Data ◽  
Data Simulation ◽  
Simulation Data ◽  
Image File ◽  
File Formats ◽  
Quantitative Image ◽  
Image Format ◽  
Calibration Parameters

AbstractExperimental data, simulation, data analysis and visualisation require image file formats that are open source and able to contain and manage quantitative data. Quantification techniques bring the new challenge of managing image calibration parameters and formulas in an open and efficient format, compatible with routine microscopy workflows. A practical approach to quantitative image format is presented and discussed here, relying on open and extensible file formats - Tagged Image File (TIF) and Extensible Metadata Platform (XMP).

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