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Proceedings Paper

Pathological microscopic image compression using lossless and progressive coding schemes
Author(s): Takayuki Nakachi; Tatsuya Fujii; Tetsuro Fujii
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Paper Abstract

This paper proposes pathological microscopic image compression schemes that suit lossless and progressive transmission. Because pathological microscopic images require very high resolution, they create heavy storage requirements and long transmission times. Image compression is desired to reduce these problems. First, we propose a lossless Karhunen-Loeve Transform (KLT) based on ladder networks. The proposed lossless KLT reduces inter-color redundancies which increases coding performance. Next we propose a progressive transmission algorithm by combining the lossless KLT and set partitioning in hierarchical trees (SPIHT) with the S+P transform. SPIHT is adopted to encode individual color-transformed components. By considering coding efficiency, the transmission bit rates of each encoded component are determined. The resulting algorithm gives high coding performance and has progressive transmission capability. When all transmitted data are decoded, decoding yields the original image. We demonstrate the performance of the proposed algorithm when applied to super high definition pathological microscopic images. All the images used in our tests have 2048x2048 pixels and 24 bits per pixel. It is shown that the coding performance of the proposed algorithm is superior to that of DCT-based JPEG with the RGB/YUV transform.

Paper Details

Date Published: 7 August 2001
PDF: 10 pages
Proc. SPIE 4323, Medical Imaging 2001: PACS and Integrated Medical Information Systems: Design and Evaluation, (7 August 2001); doi: 10.1117/12.435508
Show Author Affiliations
Takayuki Nakachi, NTT Network Innovation Labs. (Japan)
Tatsuya Fujii, NTT Network Innovation Labs. (Japan)
Tetsuro Fujii, NTT Network Innovation Labs. (Japan)


Published in SPIE Proceedings Vol. 4323:
Medical Imaging 2001: PACS and Integrated Medical Information Systems: Design and Evaluation
Eliot L. Siegel; H. K. Huang, Editor(s)

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