Abstract

Quantization, which maps real values of raw data to a series of fixed gray levels, is an inevitable step in Optical Coherence Tomography (OCT) image formation. Three new quantization methods, Minimum Distortion, Information Expansion and Maximum Entropy are applied in the specific problem. Quantization results of a capillary with milk and the femoralis of rabbit are shown in this paper. Comparisons with the present log-based methods show that a suitable quantization method significantly increases contrast, SNR and visual fineness of the final image and reduces quantization error effectively. Applicability of different quantization methods is also discussed.

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References

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  1. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J.G. Fujimoto, "Optical Coherence Tomography," Science 254, 1178 (1991).
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    [CrossRef]
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    [CrossRef]

Other

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J.G. Fujimoto, "Optical Coherence Tomography," Science 254, 1178 (1991).
[CrossRef] [PubMed]

J. M. Schmitt, "Optical coherence tomography (OCT): a review," IEEE J. Sel. Top. Quantum Electron 5, 1205 (1999).
[CrossRef]

K. R. Castleman, Digital Image Processing, (Prentice Hall, Inc., 1996).

J. S. Lim, Two-Dimensional Signal and Image Processing, (Englewood Cliffs, NJ: Prentice Hall, 1990).

R. M. Haralick, L. G. Shapiro, Computer and Robot Vision, (Reading Press: Addison-Wesley, 1993).

J. P. Dunkers, R. S. Parnas, C. G. Zimba, R. C. Peterson, K. M. Flynn, J. G. Fujimoto and B. E. Bouma, "Optical coherence tomography of glass reinforced polymer composites," Compos. Pt. A: Appl. Sci. and Mfg. 30, 139 (1999).
[CrossRef]

W. Frei, "Image enhancement by histogram hyperbolization," Comput. Graph. Image Process. 6, 286 (1977).
[CrossRef]

Y. Tao, Experimental Research of OCT System, MA's thesis of Tsinghua University, (1998).

H. Ishikawa, R. Gurses-Ozden, S. T. Hoh, H. L. Dou, J. M. Liebmann, R. Ritch, "Grayscale and proportion-corrected optical coherence tomography images," Ophthal. Surg. and Lasers 31, 223 (2000).

Jan C. A. Van der Lubbe, Information theory, (English translation Cambridge University Press, 1997).

J. Max, "Quantizing for minimum distortion," IEEE Trans. Inf. Theory IT-6, 7 (1960).
[CrossRef]

R.O. Duda, P.E. Hart, Pattern Classification and Scene Analysis, (New York: Wiley, 1973).

M. Friedman, K.Abraham, Introduction to pattern recognition: statistical, structural, neural, and fuzzy logic approaches, (River Edge, NJ, World scientific, 1999).
[CrossRef]

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Figures (2)

Fig. 1.
Fig. 1.

Resulting images of capillary with milk. (a) Direct Logarithm, (b) Truncation Logarithm, (c) Minimum Distortion, (d) Truncation Minimum Distortion, (e) Information Expansion, (f) Information Hyperbolized Expansion (g) Maximum Entropy (h) Equal Interval

Fig. 2.
Fig. 2.

Resulting images of the femoralis of rabbit. (a) Direct Logarithm, (b) Truncation Logarithm, (c) Minimum Distortion, (d) Truncation Minimum Distortion, (e) Information Expansion, (f) Information Hyperbolized Expansion (g) Maximum Entropy (h) Equal Interval

Tables (3)

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Table 1. Criterion of image evaluation

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Table 2. Image quality of capillary with milk

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Table 3. Image quality of the femoralis of rabbit

Equations (4)

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Z k = 1 2 ( q k 1 + q k ) q k Z k Z k + 1 xp ( x ) dx Z k Z k + 1 p ( x ) dx
d i = y = a i a i + 1 1 y n ( y ) y = a i a i + 1 1 n ( y ) J e = i = 0 255 y = a i a i + 1 1 n ( y ) ( y d i ) 2
ρ j = { N j n ( y ) N j + n ( y ) y m j 2 j = i 1 , i + 1 N j n ( y ) N j n ( y ) y m j 2 j = 1
S e = 1 N o ( i , j ) obj pixel ( i , j ) 2 n e = 1 N b ( i , j ) bg pixel ( i , j ) 2

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