Literature DB >> 16967804

Efficient transmission of compressed data for remote volume visualization.

Karthik Krishnan1, Michael W Marcellin, Ali Bilgin, Mariappan S Nadar.   

Abstract

One of the goals of telemedicine is to enable remote visualization and browsing of medical volumes. There is a need to employ scalable compression schemes and efficient client-server models to obtain interactivity and an enhanced viewing experience. First, we present a scheme that uses JPEG2000 and JPIP (JPEG2000 Interactive Protocol) to transmit data in a multi-resolution and progressive fashion. The server exploits the spatial locality offered by the wavelet transform and packet indexing information to transmit, in so far as possible, compressed volume data relevant to the clients query. Once the client identifies its volume of interest (VOI), the volume is refined progressively within the VOI from an initial lossy to a final lossless representation. Contextual background information can also be made available having quality fading away from the VOI. Second, we present a prioritization that enables the client to progressively visualize scene content from a compressed file. In our specific example, the client is able to make requests to progressively receive data corresponding to any tissue type. The server is now capable of reordering the same compressed data file on the fly to serve data packets prioritized as per the client's request. Lastly, we describe the effect of compression parameters on compression ratio, decoding times and interactivity. We also present suggestions for optimizing JPEG2000 for remote volume visualization and volume browsing applications. The resulting system is ideally suited for client-server applications with the server maintaining the compressed volume data, to be browsed by a client with a low bandwidth constraint.

Mesh:

Year:  2006        PMID: 16967804     DOI: 10.1109/tmi.2006.879956

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  6 in total

1.  3-D Adaptive Sparsity Based Image Compression With Applications to Optical Coherence Tomography.

Authors:  Leyuan Fang; Shutao Li; Xudong Kang; Joseph A Izatt; Sina Farsiu
Journal:  IEEE Trans Med Imaging       Date:  2015-01-01       Impact factor: 10.048

2.  Lossless Compression on MRI Images Using SWT.

Authors:  V Anusuya; V Srinivasa Raghavan; G Kavitha
Journal:  J Digit Imaging       Date:  2014-10       Impact factor: 4.056

3.  Three-Dimensional Adaptive Image Compression Concept for Medical Imaging: Application to Computed Tomography Angiography for Peripheral Arteries.

Authors:  Guillaume Fahrni; David C Rotzinger; Chiaki Nakajo; Jamshid Dehmeshki; Salah Dine Qanadli
Journal:  J Cardiovasc Dev Dis       Date:  2022-04-27

4.  A multicenter observer performance study of 3D JPEG2000 compression of thin-slice CT.

Authors:  Bradley J Erickson; Elizabeth Krupinski; Katherine P Andriole
Journal:  J Digit Imaging       Date:  2009-07-15       Impact factor: 4.056

5.  Virtual slide telepathology workstation of the future: lessons learned from teleradiology.

Authors:  Elizabeth A Krupinski
Journal:  Hum Pathol       Date:  2009-06-24       Impact factor: 3.466

6.  Sparse representations-based super-resolution of key-frames extracted from frames-sequences generated by a visual sensor network.

Authors:  Muhammad Sajjad; Irfan Mehmood; Sung Wook Baik
Journal:  Sensors (Basel)       Date:  2014-02-21       Impact factor: 3.576

  6 in total

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