<title>JPEG 2000 compression of medical imagery</title>

Abstract

A multi-institution effort was conducted to assess the visual quality performance of various JPEG 2000 (Joint Photographic Experts Group) lossy compression options for medical imagery. The purpose of this effort was to provide clinical data to DICOM (Digital Imaging and Communications in Medicine) WG IV to support recommendations to the JPEG 2000 committee regarding the definition of the base standard. A variety of projection radiographic, cross sectional, and visible light images were compressed-reconstructed using various JPEG 2000 options and with the current JPEG standard. The options that were assessed included integer and floating point transforms, scalar and vector quantization, and the use of visual weighting. Experts from various institutions used a sensitive rank order methodology to evaluate the images. The proposed JPEG 2000 scheme appears to offer similar or improved image quality performance relative to the current JPEG standard for compression of medical images, yet has additional features useful for medical applications, indicating that it should be included as an additional standard transfer syntax in DICOM.

The Current Role of Image Compression Standards in Medical Imaging

“…Compression performances of different image compression standards have been extensively studied on medical images in many studies in the existing literature [73,[77][78][79][80][106][107][108][109][110][111][112][113]. Notwithstanding, to the best of our knowledge, compression performance results in the literature are not simultaneously available for all image modalities described in Section 2 for lossless and lossy compression using the standards described in Section 3 with comparable parameters.…”

A new method for compression of remote sensing images based on an enhanced differential pulse code modulation transformation

“…We have q(i, j) from (2), which is obtained by dividing each pixel of the input image by n. The probability of the correct prediction for q(i, j) is shown in (5). R q is the number of repetitions of the new intensity value q in the divided image.…”

“…Lossless compression generally consists of transformation and entropy encoding 4,5 . There are many methods for lossless compression.…”

Joint source–channel coding: Secured and progressive transmission of compressed medical images on the Internet

“…Preliminary work has indeed stressed the need for a compression method that provides both scalability and advanced functionalities such as Region Of Interest (ROI) coding [16]. The Interleaved S+P algorithm, based on the LAR (Locally Adaptive Resolution) coding method [17], produces better coding results than the JPEG-2000 lossless mode.…”