Image File Formats: Past, Present, and Future

Wiggins, Richard H.; Davidson, H. Christian; Harnsberger, H. Ric; Lauman, Jason R.; Goede, Patricia

doi:10.1148/radiographics.21.3.g01ma25789

Cited by 81 publications

(47 citation statements)

References 4 publications

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“…Image formats maybe classified as those without any compression, such as bmp, with some lossless compression, such as png, and with lossy compression, such as jpeg [12]. The first and the second file formats are completely consistent with our algorithm.…”

Section: F Supported Image Formatsmentioning

confidence: 81%

Steganography on RGB Images Based on a “Matrix Pattern” using Random Blocks

Nilizadeh¹,

Nilchi²

2013

IJMECS

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Abstract-In this paper, we describe a novel spatial domain method for steganography in RGB images where a secret message is embedded in the blue layer of certain blocks. In this algorithm, each block first chooses a unique t 1 xt 2 matrix of pixels as a "matrix pattern" for each keyboard character, using the bit difference of neighbourhood pixels. Next, a secret message is embedded in the remaining part of the block, those without any role in the "matrix pattern" selection procedure. In this procedure, each pattern sums up with the blue layer of the image. For increasing the security, blocks are chosen randomly using a random generator. The results show that this algorithm is highly resistant against the frequency and spatial domain attacks including RS, Sample pair, X 2 and DCT based attacks. In addition, the proposed algorithm could provide more than 84.26 times of capacity comparing with a competitive method. Moreover, the results indicated that stego-image has almost 1.73 times better transparency than the competitive algorithm.

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Section: F Supported Image Formatsmentioning

confidence: 81%

Steganography on RGB Images Based on a “Matrix Pattern” using Random Blocks

Nilizadeh¹,

Nilchi²

2013

IJMECS

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“…22 This practice is similar to the concept of a table of contents. The textual information that defines the order or grouping of the visual annotations is a constant, nonvisual annotation that always exists at the first position in the outline and is a part of the information used to create the metadata of the image.…”

Section: Order or Groupingmentioning

confidence: 97%

A Methodology and Implementation for Annotating Digital Images for Context-appropriate Use in an Academic Health Care Environment

Goede

Lauman

Cochella

et al. 2004

J Am Med Inform Assoc

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A b s t r a c t Use of digital medical images has become common over the last several years, coincident with the release of inexpensive, mega-pixel quality digital cameras and the transition to digital radiology operation by hospitals. One problem that clinicians, medical educators, and basic scientists encounter when handling images is the difficulty of using business and graphic arts commercial-off-the-shelf (COTS) software in multicontext authoring and interactive teaching environments. The authors investigated and developed software-supported methodologies to help clinicians, medical educators, and basic scientists become more efficient and effective in their digital imaging environments. The software that the authors developed provides the ability to annotate images based on a multispecialty methodology for annotation and visual knowledge representation. This annotation methodology is designed by consensus, with contributions from the authors and physicians, medical educators, and basic scientists in the Departments of Radiology, Neurobiology and Anatomy, Dermatology, and Ophthalmology at the University of Utah. The annotation methodology functions as a foundation for creating, using, reusing, and extending dynamic annotations in a context-appropriate, interactive digital environment. The annotation methodology supports the authoring process as well as output and presentation mechanisms. The annotation methodology is the foundation for a Windows implementation that allows annotated elements to be represented as structured eXtensible Markup Language and stored separate from the image(s).

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“…We have chosen the TIFF format to represent the WCE color image because it has greatest strength to support the full range of image size, color depth, and resolution. It also supports various compression techniques, where lossless compression allows this format to maintain image resolution without loss of any detail related to image [24]. Image enhancement is essential before applying any techniques in image processing.…”

Section: Image Processing and Enhancementmentioning

confidence: 99%

Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images

et al. 2017

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Abstract:Wireless capsule endoscopy (WCE) is a technology developed to inspect the whole gastrointestinal tract (especially the small bowel area that is unreachable using the traditional endoscopy procedure) for various abnormalities in a non-invasive manner. However, visualization of a massive number of images is a very time-consuming and tedious task for physicians (prone to human error). Thus, an automatic scheme for lesion detection in WCE videos is a potential solution to alleviate this problem. In this work, a novel statistical approach was chosen for differentiating ulcer and non-ulcer pixels using various color spaces (or more specifically using relevant color bands). The chosen feature vector was used to compute the performance metrics using SVM with grid search method for maximum efficiency. The experimental results and analysis showed that the proposed algorithm was robust in detecting ulcers. The performance in terms of accuracy, sensitivity, and specificity are 97.89%, 96.22%, and 95.09%, respectively, which is promising.

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Image File Formats: Past, Present, and Future

Cited by 81 publications

References 4 publications

Steganography on RGB Images Based on a “Matrix Pattern” using Random Blocks

Steganography on RGB Images Based on a “Matrix Pattern” using Random Blocks

A Methodology and Implementation for Annotating Digital Images for Context-appropriate Use in an Academic Health Care Environment

Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images

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