TY - GEN
T1 - Hierarchical Clustering Linkage for Region Merging in Interactive Image Segmentation on Dental Cone Beam Computed Tomography
AU - Arifin, Agus Zainal
AU - Maryamah,
AU - Arifiani, Siska
AU - Fariza, Arna
AU - Navastara, Dini Adni
AU - Indraswari, Rarasmaya
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Interactive image segmentation has a better result than the automatic and manual image segmentation because a user can help the image segmentation algorithm by marking the sample of background and object in the image. The algorithm will merge the regions in the image based on the user marking. In interactive image segmentation, the calculation of the distance between regions and the sequence of the merging process is important to obtain an accurate segmentation result. In this paper, we proposed a new region merging strategy using hierarchical clustering based on interclass and intra-class variances for each region and neighborhood relationship. This research aims to improve the region merging strategy and it is expected to result better than the previous research that did not implement the hierarchical clustering. The process to segment an image concludes splitting the image into several regions, user marking to mark the sample of background and object, merging the region that is not marked by the user using the hierarchical clustering until the image fully segmented. The experimental results on dental cone beam computed tomography data show that the proposed method gives a more effective and efficient result in the segmentation process.
AB - Interactive image segmentation has a better result than the automatic and manual image segmentation because a user can help the image segmentation algorithm by marking the sample of background and object in the image. The algorithm will merge the regions in the image based on the user marking. In interactive image segmentation, the calculation of the distance between regions and the sequence of the merging process is important to obtain an accurate segmentation result. In this paper, we proposed a new region merging strategy using hierarchical clustering based on interclass and intra-class variances for each region and neighborhood relationship. This research aims to improve the region merging strategy and it is expected to result better than the previous research that did not implement the hierarchical clustering. The process to segment an image concludes splitting the image into several regions, user marking to mark the sample of background and object, merging the region that is not marked by the user using the hierarchical clustering until the image fully segmented. The experimental results on dental cone beam computed tomography data show that the proposed method gives a more effective and efficient result in the segmentation process.
KW - Dental cone beam computed tomography
KW - hierarchical clustering
KW - inter-class variance
KW - interactive image segmentation
KW - intra-class variance
UR - http://www.scopus.com/inward/record.url?scp=85064745028&partnerID=8YFLogxK
U2 - 10.1109/ICAITI.2018.8686738
DO - 10.1109/ICAITI.2018.8686738
M3 - Conference contribution
AN - SCOPUS:85064745028
T3 - Proceedings of ICAITI 2018 - 1st International Conference on Applied Information Technology and Innovation: Toward A New Paradigm for the Design of Assistive Technology in Smart Home Care
SP - 124
EP - 128
BT - Proceedings of ICAITI 2018 - 1st International Conference on Applied Information Technology and Innovation
A2 - Sonatha, Yance
A2 - Hidayat, Rahmat
A2 - Alanda, Alde
A2 - Humaira, MT
A2 - Rahmayuni, Indri
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Conference on Applied Information Technology and Innovation, ICAITI 2018
Y2 - 4 September 2018 through 5 September 2018
ER -