Lossless compression of medical images using Hilbert space-filling curves

Jan Yie Liang; Chih Sheng Chen; Chua Huang Huang; Li Liu

Lossless compression of medical images using Hilbert space-filling curves

Jan Yie Liang, Chih Sheng Chen, Chua Huang Huang, Li Liu

Graduate Institute of Biomedical Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A Hilbert space-filling curve is a curve of 2 ⁿ × 2 ⁿ two-dimensional space that it visits neighboring points consecutively without crossing itself. The application of Hilbert space-filling curves in image processing is to rearrange image pixels in order to enhance pixel locality. An iterative program of the Hilbert space-filling curve ordering generated from a tensor product formulation is used to rearrange pixels of medical images. We implement four lossless encoding schemes, run-length encoding, LZ77 coding, LZW coding, and Huffman coding, along with the Hilbert space-filling curve ordering. Combination of these encoding schemes are also implemented to study the effectiveness of various compression methods. In addition, differential encoding is employed to medical images to study different format of image representation to the above encoding schemes. In the paper, we report the testing results of compression ratio and performance evaluation. The experiments show that the pre-processing operation of differential encoding followed by the Hilbert space-filling curve ordering and the compression method of LZW coding followed by Huffman coding will give the best compression result.

Original language	English
Title of host publication	WMSCI 2006 - The 10th World Multi-Conference on Systemics, Cybernetics and Informatics, Jointly with the 12th International Conference on Information Systems Analysis and Synthesis, ISAS 2006 - Proc.
Pages	156-164
Number of pages	9
Volume	5
Publication status	Published - 2006
Event	10th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2006, Jointly with the 12th International Conference on Information Systems Analysis and Synthesis, ISAS 2006 - Orlando, FL, United States Duration: Jul 16 2006 → Jul 19 2006

Conference

Conference	10th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2006, Jointly with the 12th International Conference on Information Systems Analysis and Synthesis, ISAS 2006
Country/Territory	United States
City	Orlando, FL
Period	7/16/06 → 7/19/06

Keywords

Differential encoding
Hilbert space-filling curve
Huffman coding
LZ77 coding
LZW coding
Run-Length encoding

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Information Systems

Cite this

Lossless compression of medical images using Hilbert space-filling curves. / Liang, Jan Yie; Chen, Chih Sheng; Huang, Chua Huang et al.

WMSCI 2006 - The 10th World Multi-Conference on Systemics, Cybernetics and Informatics, Jointly with the 12th International Conference on Information Systems Analysis and Synthesis, ISAS 2006 - Proc.. Vol. 5 2006. p. 156-164.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Liang, JY, Chen, CS, Huang, CH & Liu, L 2006, Lossless compression of medical images using Hilbert space-filling curves. in WMSCI 2006 - The 10th World Multi-Conference on Systemics, Cybernetics and Informatics, Jointly with the 12th International Conference on Information Systems Analysis and Synthesis, ISAS 2006 - Proc.. vol. 5, pp. 156-164, 10th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2006, Jointly with the 12th International Conference on Information Systems Analysis and Synthesis, ISAS 2006, Orlando, FL, United States, 7/16/06.

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abstract = "A Hilbert space-filling curve is a curve of 2 n × 2 n two-dimensional space that it visits neighboring points consecutively without crossing itself. The application of Hilbert space-filling curves in image processing is to rearrange image pixels in order to enhance pixel locality. An iterative program of the Hilbert space-filling curve ordering generated from a tensor product formulation is used to rearrange pixels of medical images. We implement four lossless encoding schemes, run-length encoding, LZ77 coding, LZW coding, and Huffman coding, along with the Hilbert space-filling curve ordering. Combination of these encoding schemes are also implemented to study the effectiveness of various compression methods. In addition, differential encoding is employed to medical images to study different format of image representation to the above encoding schemes. In the paper, we report the testing results of compression ratio and performance evaluation. The experiments show that the pre-processing operation of differential encoding followed by the Hilbert space-filling curve ordering and the compression method of LZW coding followed by Huffman coding will give the best compression result.",

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N2 - A Hilbert space-filling curve is a curve of 2 n × 2 n two-dimensional space that it visits neighboring points consecutively without crossing itself. The application of Hilbert space-filling curves in image processing is to rearrange image pixels in order to enhance pixel locality. An iterative program of the Hilbert space-filling curve ordering generated from a tensor product formulation is used to rearrange pixels of medical images. We implement four lossless encoding schemes, run-length encoding, LZ77 coding, LZW coding, and Huffman coding, along with the Hilbert space-filling curve ordering. Combination of these encoding schemes are also implemented to study the effectiveness of various compression methods. In addition, differential encoding is employed to medical images to study different format of image representation to the above encoding schemes. In the paper, we report the testing results of compression ratio and performance evaluation. The experiments show that the pre-processing operation of differential encoding followed by the Hilbert space-filling curve ordering and the compression method of LZW coding followed by Huffman coding will give the best compression result.

AB - A Hilbert space-filling curve is a curve of 2 n × 2 n two-dimensional space that it visits neighboring points consecutively without crossing itself. The application of Hilbert space-filling curves in image processing is to rearrange image pixels in order to enhance pixel locality. An iterative program of the Hilbert space-filling curve ordering generated from a tensor product formulation is used to rearrange pixels of medical images. We implement four lossless encoding schemes, run-length encoding, LZ77 coding, LZW coding, and Huffman coding, along with the Hilbert space-filling curve ordering. Combination of these encoding schemes are also implemented to study the effectiveness of various compression methods. In addition, differential encoding is employed to medical images to study different format of image representation to the above encoding schemes. In the paper, we report the testing results of compression ratio and performance evaluation. The experiments show that the pre-processing operation of differential encoding followed by the Hilbert space-filling curve ordering and the compression method of LZW coding followed by Huffman coding will give the best compression result.

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KW - Run-Length encoding

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Y2 - 16 July 2006 through 19 July 2006

ER -

Lossless compression of medical images using Hilbert space-filling curves

Abstract

Conference

Keywords

ASJC Scopus subject areas

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