Christian Sri kusuma Aditya, Christian Sri kusuma
Teknik Informatika, Institut Teknologi Sepuluh Nopember

Published : 3 Documents
Articles

Found 1 Documents
Search
Journal : Jurnal Ilmu Komputer dan Informasi

CORTICAL BONE SEGMENTATION USING WATERSHED AND REGION MERGING BASED ON STATISTICAL FEATURES Hani`ah, Mamluatul; Aditya, Christian Sri Kusuma; Harto, Aryo; Arifin, Agus Zainal
Jurnal Ilmu Komputer dan Informasi Vol 8, No 2 (2015): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Original Source | Check in Google Scholar | Full PDF (597.002 KB)

Abstract

Research on biomedical image is a subject that attracted many researchers’ interest. This is because the biomedical image could contain important information to help analyze a disease. One of the existing researches in his field uses dental panoramic radiographs image to detect osteoporosis. The analyzed area is the width of cortical bone. To analyze that area, however, we need to determine the width of the cortical bone. This requires proper segmentation on the dental panoramic radiographs image. This study proposed the integration of watershed and region merging method based on statistical features for cortical bone segmentation on dental panoramic radiographs. Watershed segmentation process was performed using gradient magnitude value from the input image. The watershed image that still has excess segmentation could be solved by region merging based on statistical features. Statistical features used in this study are mean, standard deviation, and variance. The similarity of adjacent regions is measured using weighted Euclidean distance from the statistical feature of the regions. Merging process was executed by incorporating the background regions as many as possible, while keeping the object regions from being merged. The segmentation result has succeeded in forming the contours of the cortical bone. The average value of accuracy is 93.211%, while the average value of sensitivity and specificity is 93.858% and respectively.