A New Medical Image Enhancement Method Based on Human Visual Characteristics

Ai Bin Dong; Yun Feng Zhang; Yi Fang Liu

doi:10.4028/www.scientific.net/AMM.543-547.2543

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 543-547A New Medical Image Enhancement Method Based on...

A New Medical Image Enhancement Method Based on Human Visual Characteristics

Abstract:

Studying of image enhancement shows that the quality of image heavily relies on human visual system. In this paper, we apply this fact to design a new image enhancement method for medical images that improves the detail regions. First, the eye region of interest (ROI) is segmented; then the Un-sharp Masking (USM) is used to enhance the detail regions. Experiments show that the proposed method can effectively improve the accuracy of medical image enhancement and has a significant effect.

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Periodical:

Applied Mechanics and Materials (Volumes 543-547)

Pages:

2543-2546

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.543-547.2543

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Online since:

March 2014

Authors:

Ai Bin Dong*, Yun Feng Zhang, Yi Fang Liu

Keywords:

Image Enhancement, Medical Image, ROI, USM

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* - Corresponding Author

References

[1] Simmered P, Steinkraus D, Malvar H. On-line Adaptation Image Coding with a 2-d Tarp Filter. Proceedings of IEEE Data Compression Conference[J]. 2002. vol. 8(1): 23-32.

DOI: 10.1109/dcc.2002.999940

Google Scholar

[2] Wang Hong，Lu Ling，Que Da Shun. Image Compression Based on Wavelet Transformed Vector Quantization[J] . Beijing : Proceedings of the First International Confereneeon Machine Leamingand Cybernetics，2002(5): 35-41.

DOI: 10.1109/icmlc.2002.1175344

Google Scholar

[3] Yu fang Gao , Yang Liu. Analysis of Compression Encoding about Digital Image[D]. Beijing: Beijing University of Posts and Telecommunications, (2003).

Google Scholar

[4] Jerome M. Sha Piro. A fast Technology for Identifying Zero treesin the EZW Algorithm[J], IEEE Trans. Coe f. Aeoustv Speech Signal Proeessing. 1996(7): 11-23.

Google Scholar

[5] X. Li and M. T. Orchard, New edge-directed interpolation, IEEE Trans. on Image Process , 2001, vol. 10, no. 10, pp.1521-1526.

DOI: 10.1109/83.951537

Google Scholar

[6] Simard P, Steinkraus D, Malvar H. On-line Adaptation Image Coding with a 2-d Tarp Filter. Proceedings of IEEE Data Compression Conference[J]. 2002. vol. 8(1): 23-32.

DOI: 10.1109/dcc.2002.999940

Google Scholar

[7] YufangGao , Yang Liu. Analysis of Compression Encoding about Digital Image[D]. Beijing: Beijing University of Posts and Telecommunications, (2003).

Google Scholar

[8] Jerome M. Sha Piro. Afast Technology for Identifying Zerotreesin the EZW Algorithm[J], IEEET rans. Coef. Aeoustv Speech Signal Proeessing. 1996(7): 11-23.

Google Scholar