Segmentasi Citra Ikan Tuna Menggunakan Gradient-Barrier Watershed Berbasis Analisis Hierarki Klaster dan Regional Credibility Merging
DOI:
https://doi.org/10.24002/jbi.v7i3.661Abstract
Abstract. The main issue of object identification in tuna image is the difficulty of extracting the entire contour of tuna physical features, because it is often influenced by uneven illumination and the ambiguity of object edges in tuna image. We propose a novel segmentation method to optimize the determination of tuna region using GBW-AHK and RCM. GBW-AHK is used to optimize the determination of adaptive threshold in order to reduce over-segmented watershed regions. Then, RCM merges the remaining regions based on two merging criteria, thus it produces two main areas of segmentation, the object extraction of tuna and the background. The experimental results on 25 tuna images demonstrate that the proposed method successfully produced an image segmentation with the average value of RAE by 4.77%, ME of 0.63%, MHD of 0.20, and the execution time was 11.61 seconds.
Keywords: watershed, gradient-barrier, hierarchical cluster analysis, regional credibility merging, tuna segmentation
Abstrak. Kendala utama identifikasi objek tuna pada citra ikan tuna adalah sulitnya mengekstraksi seluruh kontur tubuh ikan, karena seringkali dipengaruhi faktor iluminasi yang tidak merata dan ambiguitas tepi objek pada citra. Penelitian ini mengusulkan metode segmentasi baru yang mengoptimalkan penentuan region objek tuna menggunakan Gradient-Barrier Watershed berbasis Analisis Hierarki Klaster (GBW-AHK) dan Regional Credibility Merging (RCM). Metode GBW-AHK digunakan untuk mengoptimalkan penentuan adaptif threshold untuk mereduksi region watershed yang over-segmentasi. Kemudian RCM melakukan penggabungan region sisa hasil reduksi berdasarkan dua syarat penggabungan hingga dihasilkan dua wilayah utama segmentasi, yakni ekstraksi objek ikan tuna dan background. Hasil eksperimen pada 25 citra ikan tuna membuktikan bahwa metode usulan berhasil melakukan segmentasi dengan nilai rata-rata relative foreground area error (RAE) 4,77%, misclassification error (ME) 0,63%, modified Hausdorff distance (MHD) 0,20, dan waktu eksekusi 11,61 detik.
Kata Kunci: watershed, gradient-barrier, analisis hierarki klaster, regional credibility merging, segmentasi tuna
References
. Arefin, M. G., Rahman, M. M., & Hossain, A. D. 2014. Automatically Gradient Threshold Estimation of Anisotropic Diffusion for Meyer’s Watershed Algorithm Based Optimal Segmentation. International Journal of Image, Graphics and Signal Processing (IJIGSP), 6(12), 26-31.
. Arifin, A. Z., & Asano, A. 2006. Image segmentation by histogram thresholding using hierarchical cluster analysis. Pattern Recognition Letters, 27(13), 1515-1521.
. Elad, M. 2002. On the origin of the bilateral filter and ways to improve it. Image Processing, IEEE Transactions on, 11(10), 1141-1151.
. Fan, C. N., & Zhang, F. Y. 2011. Homomorphic filtering based illumination normalization method for face recognition. Pattern Recognition Letters, 32(10), 1468-1479.
. Gauch, J. M. 1999. Image segmentation and analysis via multiscale gradient watershed hierarchies. Image Processing, IEEE Transactions on, 8(1), 69-79.
. Gonzalez, R. C. & Woods, R. E. Digital Image Processing. 2002. New Jersey: Prentice Hall, 6, 681.
. Hsieh, C. L., Chang, H. Y., Chen, F. H., Liou, J. H., Chang, S. K., & Lin, T. T. 2011. A simple and effective digital imaging approach for tuna fish length measurement compatible with fishing operations. Computers and Electronics in Agriculture, 75(1), 44-51.
. Ng, H. P., Huang, S., Ong, S. H., Foong, K. W. C., Goh, P. S., & Nowinski, W. L. 2008. Medical image segmentation using watershed segmentation with texture-based region merging. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE (pp. 4039-4042). IEEE.
. Pregiwati, L. A. 2015. Industri Tuna Kian Strategis, (Online), (http://kkp.go.id/index.php/pers/ industri-tuna-indonesia-kian-strategis/, diakses 6 September 2015).
. Puspita, S. D., Arifin, A. Z., & Khotimah, W. N. 2015. Penggunaan Multi Texton Co-occurrence Descriptor untuk Klasifikasi Ikan Tuna. Publikasi Ilmiah Online Mahasiswa ITS (POMITS), 4(1), 1-6.
. Sezgin, M. 2004. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic imaging, 13(1), 146-168.
. Yang, H., & Ahuja, N. 2014. Automatic segmentation of granular objects in images: Combining local density clustering and gradient-barrier watershed. Pattern Recognition, 47(6), 2266-2279.
. Yang, Y., Han, S., Wang, T., Tao, W., & Tai, X. C. 2013. Multilayer graph cuts based unsupervised color–texture image segmentation using multivariate mixed student's t-distribution and regional credibility merging. Pattern Recognition, 46(4), 1101-1124.
. Yao, H., Duan, Q., Li, D., & Wang, J. 2013. An improved K-means clustering algorithm for fish image segmentation. Mathematical and Computer Modelling, 58(3), 790-798.
. Zhang, X., Jia, F., Luo, S., Liu, G., & Hu, Q. 2014. A marker-based watershed method for X-ray image segmentation. Computer methods and programs in biomedicine, 113(3), 894-903.
Downloads
Published
Issue
Section
License
Copyright of this journal is assigned to Jurnal Buana Informatika as the journal publisher by the knowledge of author, whilst the moral right of the publication belongs to author. Every printed and electronic publications are open access for educational purposes, research, and library. The editorial board is not responsible for copyright violation to the other than them aims mentioned before. The reproduction of any part of this journal (printed or online) will be allowed only with a written permission from Jurnal Buana Informatika.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
