Segmentasi pembuluh darah pada citra retina dengan menggunakan Multi-Scale Line Detector (MSLD) dan Adaptive Morphology

Authors

  • Adithya Kusuma Whardana Ilmu Komputer, STIMIK ESQ, Jakarta
  • Deni Sutaji Teknik Informatika, Universitas Muhammadiyah Gresik, Gresik

DOI:

https://doi.org/10.26594/register.v3i1.716

Keywords:

adaptive morphology, blood vessels, Multi-Scale Line Detector, Optic disc, Segmentation, pembuluh darah, Segmentasi, segmentation, image processing

Abstract

 Pembuluh darah pada retina merupakan bagian retina yang berfungsi memberikan suplai darah dan oksigen ke dalam retina. Sehingga apabila pembuluh darah tidak tersuplai oksigen, maka dapat ditarik kesimpulan bahwa pembuluh darah retina tersebut bermasalah, banyaknya noise pada daerah pembuluh darah menyebabkan proses dalam segmentasi. Karena permasalahan yang timbul, maka dalam penelitian ini diusulkan metode segmentasi pembuluh darah dengan menggabungkan dua metode, yaitu metode Multi-Scale Line Detector (MSLD) dan Adaptive Morphology. Dari keseluruhan metode memiliki fungsi yang berbeda-beda, MSLD berfungsi dalam proses pemisahan garis yang dibentuk oleh pembuluh darah yang dalam hal ini melalui proses perubahan citra orisinal ke citra green channel, namun dalam proses sebenarnya metode MSLD kurang dalam proses segmentasi, karena timbulnya masalah disaat terjadi garis yang menyilang antara optic disc dan pembuluh darah, sehingga pada saat segmentasi garis yang menyilang tersebut tidak akan ikut disegmentasi, sehingga membutuhkan metode penambahan pada proses segmentasinya, untuk itu diperlukan metode Adaptive Morphology, sehingga saat proses segmentasi sebelumnya yang telah dilakukan dengan menggunakan MSLD bisa disempurnakan dengan menggunakan metode Adaptive Morphology. Penggabungan metode sangat efektif karena bisa menghilangkan area optic disc yang membentuk garis menyilang dengan pembuluh darah secara sempurna dengan tanpa menghilangkan area pembuluh darah, sehingga dalam proses segmentasi dapat menghasilkan tingkat akurasi 97,94%.

 

 

 

The blood vessels of the retina are part of the retina that serves to supply blood and oxygen to the retina. So if the blood vessels are not supplied oxygen, it can be concluded that the retinal blood vessels are problematic, the amount of noise in the blood vessel causes the process in segments.Karena problems arise, then in this study proposed method of blood vessel segmentation by combining two methods, namely Methods of Multi-Scale Line Detector (MSLD) and adaptive morphology. From the whole method has different functions, MSLD function in the process of separation of lines formed by blood vessels in this case through the process of changing the original image to the green channel image, but in the actual process of MSLD method is less In the process of segmentation, due to the emergence of the problem when there is a crossing line between the optic disc and blood vessels, so that when the segmentation of the crossed line will not participate in segmentation, thus requiring additional method in the process of segmentation, for that required adaptive morphology method, Previous segmentation that has been done by using MSLD can be enhanced by using adaptive morphology method. Combination method is very effective because it can eliminate the optic disc area that forms a line crossed with blood vessels perfectly without removing the blood vessel area, so in the process of segmentation can produce an accuracy of 97.94%.

 

References

Fraz, M. M., Remagnino, P., Hoppe, A., Velastin, S., Uyyanonvara, B., & Barman, S. A. (2011). A supervised method for retinal blood vessel segmentation using line strength, multiscale Gabor and morphological features. Signal and Image Processing Applications (ICSIPA), 2011 IEEE International Conference on (pp. 410-415). Kuala Lumpur: IEEE.

Nguyen, U. T., Bhuiyan, A., Park, L. A., & Ramamohanarao, K. (2013). An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recognition, 46(3), 703-715.

Sutaji, D., Fatichah, C., & Navastara, D. A. (2016). Segmentasi Pembuluh Darah Retina Pada Citra Fundus Menggunakan Gradient Based Adaptive Thresholding Dan Region Growing. Register: Jurnal Ilmiah Teknologi Sistem Informasi, 2(2), 105-116.

Thirumavalavan, S., & Jayaraman, S. (2016). An improved teaching–learning based robust edge detection algorithm for noisy images. Journal of Advanced Research, 7(6), 979–989.

Welfer, D., Scharcanski, J., Kitamura, C. M., Pizzol, M. M., Ludwig, L. W., & Marinho, D. R. (2010). Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach. Computers in Biology and Medicine, 40(2), 124-137.

Whardana, A. K., & Suciati, N. (2014). A Simple Method for Optic Disk Segmentation from Retinal Fundus Image. International Journal of Image, Graphics and Signal Processing, 6(11), 36-42.

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Published

2017-01-01

How to Cite

[1]
A. K. Whardana and D. Sutaji, “Segmentasi pembuluh darah pada citra retina dengan menggunakan Multi-Scale Line Detector (MSLD) dan Adaptive Morphology”, Register: Jurnal Ilmiah Teknologi Sistem Informasi, vol. 3, no. 1, pp. 49–56, Jan. 2017.

Issue

Vol. 3 No. 1 (2017): January

Section

Article

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