Segmentasi Citra Ikan Tuna Menggunakan Gradient-Barrier Watershed Berbasis Analisis Hierarki Klaster dan Regional Credibility Merging

Arif Fadllullah, Agus Zainal Arifin, Dini Adni Navastara


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

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