Retinal Vessel Extraction Using Dynamic Threshold And Enhancement Image Filter From Retina Fundus
Subject Areas : Image Processingerwin erwin 1 * , Tomi Kiyatmoko 2
1 - Universitas Sriwijaya
2 - Universitas Sriwijaya
Keywords: Butterworth Bandpass Filter, , Dynamic Threshold, , DRIVE, , Retinal Blood Vessels, , Segmentation, , STARE, , Buttworth Bandpass Filter, , Morphology, ,
Abstract :
In the diagnosis of retinal disease, Retinal vessels become an important role in determining certain diseases. Retina vessels are an important element with a variety of shapes and sizes, each human blood vessel also can determine the disease with various types, but the feasibility of the pattern of retinal blood vessels is very important for the advanced diagnosis process in medical retina such as detection, identification and classification. Improvement and improvement of image quality in this case is very important by focusing on extracting or segmenting the retinal veins so that parameters such as accuracy, specifications, and sensitivity can be obtained that are better and meet the advanced system. Therefore we conducted experiments in order to develop extraction of retinal images to obtain binary images of retinal vessels in the medical world using Dynamic Threshold and Butterworth Bandpass Filter. Using a database DRIVE Accuracy of 94.77%, sensitivity of 54.48% and specificity of 98.71%.
[1] N. Pratap and S. Rajeev, “Extraction of Retinal Blood Vessels by Using an Extended Matched Filter Based on Second Derivative of Gaussian,” Proc. Natl. Acad. Sci. India Sect. A Phys. Sci., 2018.
[2] A. Biran, P. S. Bidari, A. Almazroa, and K. Raahemifar, “Blood Vessels Extraction from Retinal Images Using Combined 2D Gabor Wavelet Transform with Local Entropy Thresholding and Alternative Sequential Filter,” IEEE Can. Conf. ECE, pp. 1–5, 2016.
[3] A. L. Pal, S. Prabhu, and N. Sampathila, “Extraction of Retinal Blood Vessels from Retinal Fundus Image for Computer Aided Diagnosis,” Canar. E.college, pp. 400–403, 2015.
[4] Z. Yavuz and C. Köse, “Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification,” Hindawi Int. J., vol. 2017, 2017.
[5] J. Dash, “Retinal Blood Vessels Extraction from Fundus Images Using an Automated Method,” 2018 4th Int. Conf. Recent Adv. Inf. Technol., pp. 1–5, 2018.#3 [6] J. Dash and N. Bhoi, “An Unsupervised Approach for Extraction of Blood Vessels from Fundus Images,” J. Digit. Imaging, 2018.
[7] D. Güu, “A Novel Retinal Vessel Extraction Method Based on Dynamic Scales Allocation,” Int. Conf. image, Vis. Comput., pp. 145–149, 2017.
[8] T. A. Soomro, “Retinal Blood Vessel Extraction Method Based on Basic Filtering Schemes,” IEEE Int. Conf. Image Process., 2018.
[9] M. Ben Abdallah et al., “Automatic Extraction of Blood Vessels in the Retinal Vascular Tree Using Multiscale Medialness,” Hindawi Int. J., vol. 2015, 2015.
[10] R. Kamble, “Automatic Blood Vessel Extraction Technique Using Phase Stretch Transform In Retinal Images,” 2016 Int. Conf. signal Inf. Process., 2017.
[11] B. Khomri, A. Christodoulidis, L. Djerou, and M. C. Babahenini, “Retinal blood vessel segmentation using the elite-guided multi-objective artificial bee colony algorithm,” IET Image Process., vol. 12, pp. 2163 – 2171, 2018.
[12] S. A. Amiri, “A Preprocessing Approach For Image Analysis Using Gamma Correction,” vol. 38, no. 12, 2012.
[13] F. K. P, D. Saepudin, and A. Rizal, “Analisis Contrast Limited Adaptive Histogram Equalization ( Clahe ) Dan Region Growing Dalam Deteksi Gejala Kanker Payudara Pada Citra Mammogram,” elektro, vol. 9, pp. 1–14, 2014.
[14] A. L. I. M. Reza, “Realization of the Contrast Limited Adaptive Histogram Equalization ( CLAHE ) for Real-Time Image Enhancement,” J. VLSI Signal Process., vol. 38, pp. 35–44, 2004.
[15] D. Govind, B. Ginley, and B. Lutnick, “Glomerular detection and segmentation from multimodal microscopy images using a Butterworth band-pass filter,” SPIE Med. Imaging, vol. 1058114, no. March, 2018.
[16] R. C. Gonzalez, Digital Image Processing Third Edition. 2006.
[17] Diptoneel Kayal Sreeparna Banerjee, “Dynamic Thresholding with Tabu Search for Detection of Hard Exudates in Retinal Image,” in Industry Interactive Innovations in Science, Engineering and Technology, 2017, pp. 553–560.
[18] Z. H. Chan FH, Lam FK, “Adaptive thresholding by variationalmethod,” IEEE Trans. Image Process., vol. 7, pp. 468–473, 1998.
[19] A. Ray, A. Chakraborty, D. Roy, B. Sengupta, and M. Biswas, “Blood Vessel Extraction from Fundus Image,” Emerg. Technol. Data Min. Inf. Secur., pp. 259–268, 2018.
[20] Michael Goldbaum, “STARE database,” 2003. [Online]. Available: http://cecas.clemson.edu/~ahoover/stare/.
[21] J. J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imaging, vol. 23, no. 4, pp. 501–509, 2004.
[22] M. D. Michael Goldbaum, “STructured Analysis of the Retina,” 1975. [Online]. Available: http://cecas.clemson.edu/~ahoover/stare/.
[23] H. B. Wong and G. H. Lim, “Measures of Diagnostic Accuracy: Sensitivity , Specificity , PPV and NPV,” in Proceedings of Singapore Healthcare, 2011, vol. 20, no. 4, pp. 316–318.
[24] D. M. W. Powers, “Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation,” J. Mach. Learn. Technol., pp. 37–63, 2011.
[25] R. K. B, H. Kabir, and S. Salekin, “Contrast Enhancement by Top-Hat and Bottom-Hat Transform with Optimal Structuring Element : Application to Retinal Vessel Segmentation,” Springer Int. Publ. AG 2017, pp. 533–540, 2017.