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Compression of Medical Images Using Local Neighbor Difference

Patterson, Erin Leigh
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1500463789233367

Abstract Details

2017, Master of Science (M.S.), University of Dayton, Electrical Engineering.
Medical images are an essential part to any health professional’s career when helping patients and diagnosing health concerns. Due to the need for large storage capacity and fast transferring speed, research in image compression has grown. Image compression uses the property of redundant information in the image to reduce the amount of data in the image to solve both problems of storage and transmission. For medical images, lossless compression algorithms are of interest to make sure that the reconstructed image provides the same details as the original image. This thesis presents a proposed algorithm called the Local Neighbor Difference (LND) which is a preprocessing technique to allow the redundancy in the medical image to be reduced before being sent into a commercial-off-the-shelf compressor (COTS), XZ. LND, when used in conjunction with XZ losslessly, compresses images, on average, by 6% more than XZ alone. The LND process, along with some future work, is proposed in this paper and results in a viable option for a pre-process to a compressor.
Eric Balster, Dr. (Advisor)
84 p.
Electrical Engineering
medical images; lossless; image compression; XZ;

Recommended Citations

Citations

  • Patterson, E. L. (2017). Compression of Medical Images Using Local Neighbor Difference [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1500463789233367

    APA Style (7th edition)

  • Patterson, Erin. Compression of Medical Images Using Local Neighbor Difference. 2017. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1500463789233367.

    MLA Style (8th edition)

  • Patterson, Erin. "Compression of Medical Images Using Local Neighbor Difference." Master's thesis, University of Dayton, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1500463789233367

    Chicago Manual of Style (17th edition)

dayton1500463789233367
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This open access ETD is published by University of Dayton and OhioLINK.