J Korean Soc Med Inform.  2009 Jun;15(2):227-234. 10.4258/jksmi.2009.15.2.227.

A Study on Pupil and Iris Segmentation of the Anterior Segment of the Eye

Affiliations
  • 1Biomedical Engineering Branch, Division of Cancer, National Cancer Center, Korea. kimkg@ncc.re.kr
  • 2Department of Ophthalmology, Seoul National University College of Medicine, Korea.
  • 3Seoul National University Bundang Hospital, Korea.

Abstract


OBJECTIVE
The goal of this study was to develop a novel pupil and iris segmentation algorithm. We evaluated segmentation performance based on a fractal model. Two methods were compared: Daugman's and our new proposed method. METHODS: We received 200 anterior segment images with 3,872x2,592 pixels. Here we present an active contour model that accurately detects pupil boundaries in order to improve the performance of segmentation systems. We propose a method that uses iris segmentation based on a fractal model. We compared the performance of Daugman's method and the proposed new method and statistically analyzed the results. RESULTS: We manually compared segmentation with the Daugman's method and the new proposed method. The findings showed that the proposed segmentation accuracy was about 2.5 percent higher than Daugman's method. There was a significant difference (p<0.05) between the under and over data between the two methods. CONCLUSION: The results of this study show that the new proposed method was more accurate than the conventional method for the measurement of segmentation of the eye by CAD (Computer-aided Diagnosis).

Keyword

Pupil; Iris; Segmentation; Computer-aided Diagnosis; Image Processing

MeSH Terms

Eye
Fractals
Iris
Pupil

Figure

  • Figure 1 Flowchart of proposed method

  • Figure 2 (a) Original image, (b) Image of hue, (c) Image of saturation, (d) Image of value

  • Figure 3 Pupil segmentation result

  • Figure 4 Iris segmentation based on radiuses directions

  • Figure 5 The result of iris segmentation of fractal dimension method

  • Figure 6 The ratio of over segmentation

  • Figure 7 The comparison between Daugman's method and proposed method

  • Figure 8 The ratio of under segmentation

  • Figure 9 The comparison between Daugman's method and proposed method


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Reference

1. Lim JI, LaBree L, Nichols T, Cardenas I. A comparison of digital nonmydriatic fundus imaging with standard 35-millimeter slides for diabetic retinopathy. Ophthalmology. 2000. 107(5):866–870.
Article
2. Yen KG, Hess D, Burke B, Johnson RA, Feuer WJ, Flynn JT. The optimum time to employ telephotoscreening to detect retinopathy of prematurity. Trans Am Ophthalmol Soc. 2000. 98:145–150. discussion 150-151.
3. van Norren D, van de Kraats J. Retinal densitometer with the size of a fundus camera. Vision Res. 1989. 29(3):369–374.
Article
4. Tuulonen A, Airaksinen PJ, Montagna A, Nieminen H. Screening for glaucoma with a non-mydriatic fundus camera. Acta Ophthalmol (Copenh). 1990. 68(4):445–449.
Article
5. Zeimer R, Zou S, Meeder T, Quinn K, Vitale S. A fundus camera dedicated to the screening of diabetic retinopathy in the primary-care physician's office. Invest Ophthalmol Vis Sci. 2002. 43(5):1581–1587.
6. Gili Manzanaro P, Carrasco Font C, Martin Rodrigo JC, Yanguela Rodilla J, Arias Puente A. Digital analysis of the optic disc with fundus camera: a study of variability. Arch Soc Esp Oftalmol. 2004. 79(3):125–130.
7. Schmitz-Valckenberg S, Fleckenstein M, Gobel AP, Sehmi K, Fitzke FW, Holz FG, et al. Evaluation of autofluorescence imaging with the scanning laser ophthalmoscope and the fundus camera in age-related geographic atrophy. Am J Ophthalmol. 2008. 146(2):183–192.
Article
8. Daugman J. High confidence Recognition of Persons by Rapid Analysis of Iris Texture, IEEE Conference Publication. 1995. 05. 244–251. No. 408.
9. Daugman J. New methods in iris recognition. IEEE Trans Syst Man Cybern B Cybern. 2007. 37(5):1167–1175.
Article
10. Huang H, Hu G. Iris recognition based on adjustable scale wavelet transform. Conf Proc IEEE Eng Med Biol Soc. 2005. 7:7533–7536.
11. Monro DM, Rakshit S, Zhang D. DCT-based iris recognition. IEEE Trans Pattern Anal Mach Intell. 2007. 29(4):586–595.
Article
12. Klemencic A, Kovacic S, Pernus F. Automated segmentation of muscle fiber images using active contour models. Cytometry. 1998. 32(4):317–326.
Article
13. Bogdanova I, Bresson X, Thiran JP, Vandergheynst P. Scale space analysis and active contours for omnidirectional images. IEEE Trans Image Process. 2007. 16(7):1888–1901.
Article
14. Parker JR. Algorithms for image processing and computer vision. 1997. WILEY COMPUTER PUBLISHING, 1997.
15. Gonzalez RC. Digital image processing using MatLab. 2004. Seoul: ITC..
16. Otsu N. A threshold selection method from gray_level histograms. IEEE Trans Systems Man and Cybernetics. 1979. 79:62–66.
Article
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