&lt;title&gt;Image compression based on multiresolution decomposition: application to chest radiographs&lt;/title&gt;

Kenji Shimizu; J Ikezoe; J Arisawa; Takeshi Johkoh; Nobuaki Kohno; Shigeyuki Yoshida; Kazuya Ichikado; Hironobu Nakamura; Shinichi Tamura; Naoki Sugiyama; Chikayoshi Yuzawa

doi:10.1117/12.238474

JOURNAL ARTICLE

<title>Image compression based on multiresolution decomposition: application to chest radiographs</title>

Kenji Shimizu J Ikezoe J Arisawa Takeshi Johkoh Nobuaki Kohno Shigeyuki Yoshida Kazuya Ichikado Hironobu Nakamura Shinichi Tamura Naoki Sugiyama Chikayoshi Yuzawa

Year: 1996 Journal: Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE Vol: 2707 Pages: 450-455 Publisher: SPIE

DOI: 10.1117/12.238474

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Abstract

We have designed an image compression scheme based on multiresolution decomposition using the quadrature mirror filters (QMFs), and applied it to chest radiographs. In order to assess the preservation of diagnostic image quality, we performed image review study using a diagnostic workstation. From the result of review by diagnostic radiologists, our method may be available in clinical practice.

Keywords:

Radiography Image compression Image quality Computer science Computer vision Quadrature mirror filter Artificial intelligence Data compression Decomposition Compression (physics) Medical physics Image (mathematics) Radiology Medicine Nuclear medicine Image processing Filter (signal processing) Materials science Low-pass filter

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Advanced Data Compression Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

AI in cancer detection

Physical Sciences → Computer Science → Artificial Intelligence

<title>Image compression based on multiresolution decomposition: application to chest radiographs</title>

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