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

Abstract

The joint source-channel coding system proposed in this paper has two aims: lossless compression with a progressive mode and the integrity of medical data, which takes into account the priorities of the image and the properties of a network with no guaranteed quality of service. In this context, the use of scalable coding, Locally Adapted Resolution (LAR) and a discrete and exact Radon transform, known as the Mojette transform, meets this twofold requirement. In this paper, details of this joint coding implementation are provided as well as a performance evaluation with respect to the reference CALIC coding and to unequal error protection using Reed-Solomon codes.

Joint Source/Channel Coding for Prioritized Wireless Transmission of Multiple 3-D Regions of Interest in 3-D Medical Imaging Data

“…In a more general framework, such as 2-D medical image transmission over error-prone channels, important source coding methods that incorporate channel coding have been reported [15], [16]. In [15], the authors present a JSCC method for transmission over the Internet protocol.…”

“…In [15], the authors present a JSCC method for transmission over the Internet protocol. The method is based on locally adaptive resolution (LAR) compression and Mojette transform, and features RoI coding.…”

Medical high-resolution image sharing and electronic whiteboard system: A pure-web-based system for accessing and discussing lossless original images in telemedicine

“…Since a household display device with limited size and colors cannot display all the information while focusing on the image details, our study is built on the basis of solution 3, using the idea of tile-based on-demand transmission, to keep lossless large image transmission. However, [17,18] only supported a 4× ratio zooming which was limited by basic tile-pyramid model, which is also the ideal scaling factor of JPEG2000 [20,21]. While 4× ratio zooming does not affect users reading extreme HRIs such as satellite images and microscope images [18,23,24], in practice it may be not convenient to read medical HRIs with such resolutions as 3004 × 2480.…”

Preserving Data Integrity of Encoded Medical Images: The LAR Compression Framework

“…In other words, compensating IP packet loss also requires a UEP process, which uses an exact and discrete Radon transform, called the Mojette transform [12]. The frame-like definition of this transform allows redundancies that can be further used for image description and image communication (Figure 22), for QoS purposes.…”