DICOM demystified: A review of digital file formats and their use in radiological practice

This paper highlights the differences among the most common file formats used for storing digital radiological images. It promotes the proper use of these formats to guarantee easy manipulation in handling the most typical practical applications in daily radiological practice. The authors provide a simple yet exhaustive introduction to the concept of "file format" and describe the algorithms and main features of the most common formats (BMP, JPEG, GIF, DICOM, TIF, PNG) and Portable Network Graphics (PNG).The different formats are compared in terms of dimension, quality, portability and with reference to the following specific needs: electronic communications, publication on the World Wide Web, presentation of electronic posters, video presentations for teaching and manuscript publishing. We also illustrate how to handle the various formats with the programmes supplied with standard software installations.The large number of digital applications of image file formats calls for a simplification in daily radiological practice. We recommend the use of JPEG and PNG for electronic communications; PNG and GIF for publication on the worldwide web; JPEG and PNG for electronic poster presentations; DICOM, PNG and JPEG for teaching presentations; TIF and PNG for printing on paper.

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“…The following sections review the most common image file formats, which are bitmap (BMP, JPEG, GIF, DICOM, TIFF, PNG) [1][2][3][4][5][6][7][8].…”

Section: Image Formatsmentioning

confidence: 99%

Proper use of common image file formats in handling radiological images

et al. 2009

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“…In DICOM (Digital communication in medicine) [16] mammogram images have different contrasts and entropies at each set of pixels in complex, dense and diffuse set of pixels because mostly in all types of breast tissues are found with varying behaviors from many perspectives in terms of shapes, sizes and other properties (such as malignant masses, lesions, macro and micro calcifications). Thus these types of circumstances produce lots of confusions for the radiologists during the interpretation of mammograms.…”

Section: A Calcification Feature Extraction Deep Neural Network Algmentioning

confidence: 99%

Intelligent System for Detection of Micro-Calcification in Breast Cancer

Rehman¹,

Ahmed²,

Ahmed³

2017

ijacsa

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Abstract-Recently; medical image mining has become one of the well-recognized research area(s) of machine learning and artificial intelligence techniques have been vastly used in various computer added diagnostic systems. Specifically; breast cancer classification problem is considered as one of the most significant problems. For instance, complex, diverse and heterogamous malignant features of micro-calcification in DICOM (Digital Communication in Medicine) images of mammography are very difficult to classify because the persistence of noise in mammogram images creates lots of confusions for doctors. In order to reduce the chances of misdiagnosis and to discernment the difference between malignant and benign lesions of microcalcification this paper proposes a system so called "Intelligent System For Detection of Micro-Calcification in Breast Cancer" by considering all above stated problems. Overall our system comprises over three main stages. In first stage, adaptive threshold algorithm is used to reduce the noise, and canny edge detection algorithm is used to detect the edges of every macro or micro classification. In second stage, deginated as feature selection is done by using auto-crop algorithm, which crops all types of calcifications and lesions by proposed algorithm so called CFEDNN (Calcification Feature Extraction Deep Neural Networks) which is designed to avoid the manual ROIs (Region of Interest). Decision model is constructed by using DNN (Deep Neural Networks) and the best classification accuracy is measured as 95.6%.

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“…DICOM is not only a file format but also a pervasive standard containing protocols for communication with modalities and managing storage of files [4]. The software solution that is compliant with DICOM is called a picture archiving and communication system (PACS).…”

Section: Dicommentioning

confidence: 99%

Digital Autopsy: Popular Tools for an Unpopular Procedure

Islam¹,

Khan²,

Ikematsu³

et al. 2018

AJFSFM

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Digital autopsy is the future of postmortem examination of the human body through digital visualization. Imaging modalities such as CT scanners and MRI scanners use Xrays and magnetic fields to visualize the structures within the human body. The outputs from these modalities are obtained in a DICOM (Digital Imaging and Communications of Medicine) format. They are in gray-scale consisting of information in every slice taken during the scan. This is rendered and visualized as the full body in a digital format during the digital autopsy procedure. The visualization rendering produces a three-dimensional body, further presented in colour format based on each structure of the human organ system. Digital autopsy is a technology which allows pathologists to navigate and explore deeper into the human body. It allows analysis to be done in both two-dimensional and threedimensional perspectives. Digital autopsy involves analysis of human pathology and anatomical findings for diagnostic purposes. It can also be used for education and research purposes. Another advantage of digital autopsy is the availability of information and data that can be easily and securely transferred to a different digital autopsy facility at a different location. Thus, digital autopsy provides an efficient, fast, cheaper and accurate post-mortem data enabling a forensic pathologists to play a more effective role in the criminal justice system. DICOMThe images from all modalities and medical imaging devices are stored as DICOM files. DICOM is not only a file format but also a pervasive standard containing pro- The intensity in almost all modality outputs is monochrome, mainly in gray (black and white). These values were mapped to real tissues of the human body in Hounsfield units (HU) [7]. In addition, a set of default measurements defined by Sir Godfrey N Hounsfield for CT scanners is available for matching and viewing these organs and tissues ( Table-1). Digital Autopsy Visualization Three-Dimension (3D) VisualizationThe most important technique to transform layers of CT image data into 3D images (models) is the rendering technique comprising the following three steps [8]:

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DICOM demystified: A review of digital file formats and their use in radiological practice

Cited by 129 publications

References 12 publications

Proper use of common image file formats in handling radiological images

Proper use of common image file formats in handling radiological images

Intelligent System for Detection of Micro-Calcification in Breast Cancer

Digital Autopsy: Popular Tools for an Unpopular Procedure

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