Feature Extraction for Retina Image Based on Difference Approaches

Erwin Erwin; Saparudin Saparudin; Arum Cantika Putri; Hidayat Hidayat; Fifi Hariyani

doi:10.18495/comengapp.v7i3.275

[1] I. N. Figueiredo et al., “Automated retina identification based on multiscale elastic registration,” Comput. Biol. Med., vol. 79, pp. 130–143, 2016.
[2] R. Vega, G. Sanchez-Ante, L. E. Falcon-Morales, H. Sossa, and E. Guevara, “Retinal vessel extraction using Lattice Neural Networks with dendritic processing,” Comput. Biol. Med., vol. 58, pp. 20–30, 2015.
[3] D. Zhou and H. Zhou, “Minimisation of local within-class variance for image segmentation,” IET Image Process., vol. 10, no. 2, pp. 608–615, 2016.
[4] X. Zheng, H. Ye, and Y. Tang, “Image bi-level thresholding based on gray level-local variance histogram,” Entropy, vol. 19, no. 5, pp. 1–8, 2017.
[5] L. Xiong, H. Li, and L. Xu, “An enhancement method for color retinal images based on image formation model,” Comput. Methods Programs Biomed., vol.
143, pp. 137–150, 2017.
[6] B. Al-Bander, W. Al-Nuaimy, B. M. Williams, and Y. Zheng, “Multiscale
sequential convolutional neural networks for simultaneous detection of fovea and optic disc,” Biomed. Signal Process. Control, vol. 40, pp. 91–101, 2018.
[7] F. M. Villalobos-Castaldi, E. M. Felipe-Riverón, and L. P. Sánchez-Fernández, “A fast, efficient and automated method to extract vessels from fundus images,” J. Vis., vol. 13, no. 3, pp. 263–270, 2010.
[8] Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, “A cross-modality learning approach for vessel segmentation in retinal images,” IEEE Trans. Med. Imaging, vol. 35, no. 1, pp. 109–118, 2016.
[9] Q. Li, J. You, and D. Zhang, “Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses,” Expert Syst. Appl., vol. 39, no. 9, pp. 7600–7610, 2012.
[10] I. Lázár and A. Hajdu, "Segmentation of retinal vessels by means of directional response vector similarity and region growing,” Comput. Biol. Med., vol. 66, pp. 209–221, 2015.
[11] B. Yin et al., “Vessel extraction from non-fluorescein fundus images using orientation-aware detector,” Med. Image Anal., vol. 26, no. 1, pp. 232–242, 2015.
[12] S. Manikandan, K. Ramar, M. Willjuice Iruthayarajan, and K. G. Srinivasagan, “Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm,” Meas. J. Int. Meas. Confed., vol. 47, no. 1, pp. 558–
568, 2014.
[13] S. Kotte, P. Rajesh Kumar, and S. K. Injeti, “An efficient approach for optimal multilevel thresholding selection for gray scale images based on improved differential search algorithm,” Ain Shams Eng. J., 2015.
[14] M. H. Mozaffari and W.-S. Lee, “Convergent heterogeneous particle swarm optimisation algorithm for multilevel image thresholding segmentation,” IET Image Process., vol. 11, no. 8, pp. 605–619, 2017.
[15] D. Mishra, I. Bose, U. Chandra De, and B. Pradhan, “A multilevel image
thresholding using particle swarm optimization,” Int. J. Eng. Technol., vol. 6, no. 2, pp. 1204–1211, 2014.
[16] Y. Liu, C. Mu, W. Kou, and J. Liu, “Modified particle swarm
optimizationbased multilevel thresholding for image segmentation,” Soft Comput., vol. 19, no. 5, pp. 1311–1327, 2015.
[17] H. Maryam, A. Mustapha, and J. Younes, “A multilevel thresholding method for image segmentation based on multiobjective particle swarm optimization,” IEEE, pp. 0–5, 2017.
[18] K. S. Sreejini and V. K. Govindan, “Improved multiscale matched filter for
retina vessel segmentation using PSO algorithm,” Egypt. Informatics J., vol.
16, no. 3, pp. 253–260, 2015.
[19] L. Câmara Neto, G. L. B. Ramalho, J. F. S. Rocha Neto, R. M. S. Veras, and F. N. S. Medeiros, “An unsupervised coarse-to-fine algorithm for blood vessel segmentation in fundus images,” Expert Syst. Appl., vol. 78, pp. 182–192, 2017.
[20] Y. Gavet et al., “Dissimilarity criteria and their comparison for quantitative evaluation of image segmentation : application to human retina vessels,” Mach. Vis. Appl., vol. 25, no. 8, pp. 1953–1966, 2014.
[21] G. Azzopardi, N. Strisciuglio, M. Vento, and N. Petkov, “Trainable COSFIRE filters for vessel delineation with application to retinal images,” Med. Image Anal., vol. 19, no. 1, pp. 46–57, 2015.
[22] M. Hassan, M. Amin, I. Murtaza, A. Khan, and A. Chaudhry, “Robust Hidden
Markov Model based intelligent blood vessel detection of fundus images,”
Comput. Methods Programs Biomed., vol. 151, pp. 193–201, 2017.
[23] D. Pandey, X. Yin, H. Wang, and Y. Zhang, “Accurate vessel segmentation
using maximum entropy incorporating line detection and phase-preserving
denoising,” Comput. Vis. Image Underst., vol. 155, pp. 162–172, 2017.
[24] M. E. Gegúndez-Arias, A. Aquino, J. M. Bravo, and D. Marín, “A function for quality evaluation of retinal vessel segmentations,” IEEE Trans. Med. Imaging, vol. 31, no. 2, pp. 231–9, 2012.
[25] B. Zhang, L. Zhang, L. Zhang, and F. Karray, “Retinal vessel extraction by matched filter with first-order derivative of Gaussian,” Comput. Biol. Med., vol. 40, no. 4, pp. 438–445, 2010.
[26] U. T. V. Nguyen, A. Bhuiyan, L. A. F. Park, and K. Ramamohanarao, “An
effective retinal blood vessel segmentation method using multi-scale line detection,” Pattern Recognit., vol. 46, no. 3, pp. 703–715, 2013.

Abstract viewed - 1159 times
PDF downloaded - 1004 times

Affiliations

Erwin Erwin
University of Sriwijaya

Saparudin Saparudin
Universitas Sriwijaya

Arum Cantika Putri
Universitas Sriwijaya

Hidayat Hidayat
Universitas Sriwijaya

Fifi Hariyani
Universitas Sriwijaya

Content Top

Feature Extraction for Retina Image Based on Difference Approaches

Erwin Erwin ,
Saparudin Saparudin ,
Arum Cantika Putri ,
Hidayat Hidayat ,
Fifi Hariyani

Vol 7 No 3 (2018)

DOI 10.18495/comengapp.v7i3.275

Submitted: Jul 31, 2018

Published: Oct 18, 2018

Abstract

Automatic disease diagnosis using biometric images is a difficult job due to image distortion, such as the presence of artifacts, less or excessive light, narrow vessel visibility and differences in inter-camera variability that affect the performance of an approaches. Almost all extraction methods in the blood vessels in the retina produce the main part of the vessel with no patalogical environment. However, an important problem for this method is that extraction errors occur if they are too focused on the thin vessels, the wide vessels will be more detectable and also artificial vessels that may appear a lot. In addition, when focusing on a wide vessel, the extraction of thin vessels tends to disappear and is low. Based on our research, different treatments are needed to extract thin vessels and wide vessels both visually and in contrast. This study aims to adopt feature extraction strategies with different techniques. The method proposed in segmentation and extraction with three different approaches, namely the pattern of shape, color, and texture. Testing segmentation and feature extraction using STARE datasets with five classes of diseases namely Choroidal Neovascularization, Branch Retinal Vein Occlusion, Histoplasmosis, Myelinated Nerve Fibers, and Coats. Image enhancement on Myelinated Nerve disease Fiber is the best result from the image of other diseases with the highest value of PSNR of 35.4933 dB and the lowest MSE of 0.0003 means that the technique is able to repair objects well. The main significance of this work is to increase the quality of segmentation results by applying the Otsu method. Testing of segmentation results shows improvements with the proposed method compared to other methods. Furthermore, the application of different feature extraction methods will get information on disease classification features based on patterns of suitable shapes, colors, and textures.