Detection of Blood Vessels in Retina Images using Gray Level Grouping Method
الموضوعات : Majlesi Journal of Telecommunication Devices
Majid Eskandari Shahraki
1
,
Mehran Emadi
2
1 - Master student, Faculty of Electrical Engineering , Islamic Azad University , Mobarakeh Branch , Mobarakeh, Isfahan, Iran
2 - Assistant Professor, Faculty of Electrical Engineering,Islamic Azad University, Mobarakeh Branch, Mobarakeh, Isfahan, Iran
الکلمات المفتاحية: Feature extraction vector, Gray level grouping, Perceptron Neural Network, Retinal images, Histogram modulation,
ملخص المقالة :
The main part of the eye is the retina covering the entire back section of the eye. Eye disease is one of the most important cause of disability and even death in developed countries as well as in developing countries. Disorders created in the retina that occur due to special diseases can be detected by specific retinal images. Studying the variations in retinal photos in a special time could help physicians to diagnose the associated diseases. In this paper, the detection of blood veins in retina photos was investigated. For this purpose, first a new method is proposed to promote the quality of retina photos by combining the histogram adjustment and gray level grouping. We use the feature vector to classify the pixels. Next, a method for classifying the images based on the feature extraction vector is required. The use of neural networks is one of the best and most widely used methods of machine learning for classification. We used a 3-layer Perceptron to classify pixels.
[1] S. Shahbeig, “Automatic and quick blood vessels extraction algorithm in retinal images,” IET Image Processing, 2013, Vol. 7, pp. 392-400.
[2] N. Pattona, and et al., “Retinal image analysis: concepts, applications and potential,” Progress in Retinal and Eye Research, 2006, Vol. 25, pp. 99-127.
[3] R. Larsen, M. Nielsen, and J. Sporring, “Medical Image Computing and Computer-Assisted Intervention - MICCAI,” 9th Int. Conf. Proc, Part I, 2006.
[4] S. Chaudhuri, and et al, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Trans. Med. Img., 1989, Vol. 8, No. 3, pp. 263-296.
[5] L. Gang, O. Chutatape, and S. M. Krishnan, “Detection and measurement of retinal vessels in fundus images using amplitude modified second-order gaussian filter,” IEEE Trans. on Biomedical Engineering, 1989, Vol. 49, NO. 2, pp. 168-172
[6] J. Staal, and et al., “Ridge based vessel segmentation in color images of the retina,” IEEE Trans. Med. Img., 2004, Vol. 23, No. 4, pp. 501-509.
[7] J. V. B. Soares, and et al. , “Using the 2-D Morlet wavelet with supervised classification for retinal vessel segmentation,” in Proc.
[8] 18th.Brazilian.Symp. Comput. Graphics Image Process, 2005.
[9] W. T. E. Freeman, and W. T. Adelson, “The design and use of steerable filters,” IEEE Trans. Med. Img., 1991, Vol.13, No. 9, pp. 891-906.
[10] X. A. Gao, and et al., , “A method of vessel tracking for vessel diameter measurement on retinal images,” In proc. of ICIP'01, 2001, pp. 881-884.
[11] M. Lalonde, L. Gagnon, and M. C. Boucher, “Non-recursive paired tracking for vessel extraction from retinal images,” Proc. of the Conference Vision Interface 2000, pp. 61-68.
[12] A. Can, “Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms,” IEEE Trans. on Information Technology in Biomedicine, 1999, Vol.3, No.2, pp. 125-138.
[13] Y. Hatanaka, and et al, “Automated analysis of the distributions and geometries of blood vessels on retinal fundus images,” Proc. SPIE Med. Imag. 2004: Image Process., 2004, Vol. 5370, pp. 1621-1628.
[14] A. M. Mendonca, and A. Campilho, “Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction,” IEEE Trans. Med. Img., 2006, Vol. 25, No. 9, pp. 1200-1213.
[15] E. Candes, “Harmonic analysis of neural networks,” Appl. Comput. Harmon. Anal., 1999, Vol. 6, pp. 197-218.
[16] S. Garg, J. Sivaswamy, and S. Chandra, “Unsupervised curvature-based retinal vessel segmentation,” 4th. IEEE. Int. Symp. Biomed. Img, 2007, pp. 344-347.
[17] F. Chui, and et al., “Feature Extraction for Classification from Images: A Look at the Retina,” IEEE. Int. Symp. Ubiquitous Multimedia.Comp, 2008, pp.93-98.
[18] D. Marin, and et al., “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants based features,” IEEE Trans. Med. Img., 2011, Vol. 30, No. 1, pp. 146–158.
