Contrast enhancement in retinal image via multi-scale geometrical analysis

Abstract

We present a new multi-scale geometrical analysis method for ophthalmic image contrast enhancement based on the contourlet transform. The contourlet transform has better performance in representing edges than wavelets due to its anisotropy and directionality, and is therefore well-suited for multiscale edge enhancement. We modify the contourlet coefficients in corresponding subband and take the noise into account for more precise reconstruction and better visualization. We compare this approach with enhancement based on the curvelet transform, and the traditional Histogram Stretching (HS). Our findings are that contourlet based enhancement out-performs other enhancement methods on low contrast and dynamic range images, and can clearly identify the vessels and nerves in an ophthalmic image.

References

[1] CandèsE. J., DonohoD. L.. Curvelets—a surprisingly effective nonadaptive representation for objects with edges, 1999, Nashville, TN, Vanderbilt University Press

[2] DoM. N., VetterliMartin. IEEE Trans on Image processing, 2005, 14: 2091-2091

[3] VeldeK. V.. Proc. Int. Conf. Image Processing, 1999, 3: 584-584

[4] StackJ. L., MurtaghF.. IEEE Trans on Image processing, 2003, 12: 706-706

[5] PhoongS.-M., KimC. W., VaidyanathanP. P., AnsariR.. IEEE Trans. Signal Proc., 1995, 43: 649-649