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Oncologic image compression using both wavelet and masking techniques.

Publication ,  Journal Article
Yin, FF; Gao, Q
Published in: Med Phys
December 1997

A new algorithm has been developed to compress oncologic images using both wavelet transform and field masking methods. A compactly supported wavelet transform is used to decompose the original image into high- and low-frequency subband images. The region-of-interest (ROI) inside an image, such as an irradiated field in an electronic portal image, is identified using an image segmentation technique and is then used to generate a mask. The wavelet transform coefficients outside the mask region are then ignored so that these coefficients can be efficiently coded to minimize the image redundancy. In this study, an adaptive uniform scalar quantization method and Huffman coding with a fixed code book are employed in subsequent compression procedures. Three types of typical oncologic images are tested for compression using this new algorithm: CT, MRI, and electronic portal images with 256 x 256 matrix size and 8-bit gray levels. Peak signal-to-noise ratio (PSNR) is used to evaluate the quality of reconstructed image. Effects of masking and image quality on compression ratio are illustrated. Compression ratios obtained using wavelet transform with and without masking for the same PSNR are compared for all types of images. The addition of masking shows an increase of compression ratio by a factor of greater than 1.5. The effect of masking on the compression ratio depends on image type and anatomical site. A compression ratio of greater than 5 can be achieved for a lossless compression of various oncologic images with respect to the region inside the mask. Examples of reconstructed images with compression ratio greater than 50 are shown.

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Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

December 1997

Volume

24

Issue

12

Start / End Page

2038 / 2042

Location

United States

Related Subject Headings

  • Tomography, X-Ray Computed
  • Phantoms, Imaging
  • Pelvis
  • Nuclear Medicine & Medical Imaging
  • Neoplasms
  • Magnetic Resonance Imaging
  • Image Interpretation, Computer-Assisted
  • Humans
  • Female
  • Breast Neoplasms
 

Citation

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Yin, F. F., & Gao, Q. (1997). Oncologic image compression using both wavelet and masking techniques. Med Phys, 24(12), 2038–2042. https://doi.org/10.1118/1.598118
Yin, F. F., and Q. Gao. “Oncologic image compression using both wavelet and masking techniques.Med Phys 24, no. 12 (December 1997): 2038–42. https://doi.org/10.1118/1.598118.
Yin FF, Gao Q. Oncologic image compression using both wavelet and masking techniques. Med Phys. 1997 Dec;24(12):2038–42.
Yin, F. F., and Q. Gao. “Oncologic image compression using both wavelet and masking techniques.Med Phys, vol. 24, no. 12, Dec. 1997, pp. 2038–42. Pubmed, doi:10.1118/1.598118.
Yin FF, Gao Q. Oncologic image compression using both wavelet and masking techniques. Med Phys. 1997 Dec;24(12):2038–2042.

Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

December 1997

Volume

24

Issue

12

Start / End Page

2038 / 2042

Location

United States

Related Subject Headings

  • Tomography, X-Ray Computed
  • Phantoms, Imaging
  • Pelvis
  • Nuclear Medicine & Medical Imaging
  • Neoplasms
  • Magnetic Resonance Imaging
  • Image Interpretation, Computer-Assisted
  • Humans
  • Female
  • Breast Neoplasms