TY - GEN
T1 - Analysis on semi-automated knee cartilage segmentation model using inter-observer reproducibility
T2 - 7th International Conference on Bioscience, Biochemistry and Bioinformatics, ICBBB 2017
AU - Hong Seng, Gan
AU - Rosidi, Rasyiqah Annani Mohd
AU - Sayuti, Khairil Amir
AU - Khaizi, Aida Syafiqah Ahmad
AU - Karim, Ahmad Helmy Abdul
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/1/21
Y1 - 2017/1/21
N2 - Osteoarthritis is the most common joint disease without any effective cure to halt its' prevalence. Various medical image segmentation techniques have been proposed to extract knee cartilage from magnetic resonance image in order to identify suitable biomarkers to study the progression of osteoarthritis. In this paper, we propose the use of a random walks knee cartilage segmentation model and analyze the model's accuracy using inter-observer reproducibility. For instance, the proposed model has exhibited promising reproducibility compared to manual segmentation in both normal and diseased categories. In normal cartilage segmentation, the proposed model has shown reproducibility index of 0.93±0.022 while in diseased cartilage segmentation, the proposed model has shown reproducibility index of 0.90±0.049. The results suggest that random walks semi-automated segmentation model reduces the level of ambiguity experienced by manual segmentation model; thus establishing the technique as suitable computerized segmentation technique for knee cartilage segmentation.
AB - Osteoarthritis is the most common joint disease without any effective cure to halt its' prevalence. Various medical image segmentation techniques have been proposed to extract knee cartilage from magnetic resonance image in order to identify suitable biomarkers to study the progression of osteoarthritis. In this paper, we propose the use of a random walks knee cartilage segmentation model and analyze the model's accuracy using inter-observer reproducibility. For instance, the proposed model has exhibited promising reproducibility compared to manual segmentation in both normal and diseased categories. In normal cartilage segmentation, the proposed model has shown reproducibility index of 0.93±0.022 while in diseased cartilage segmentation, the proposed model has shown reproducibility index of 0.90±0.049. The results suggest that random walks semi-automated segmentation model reduces the level of ambiguity experienced by manual segmentation model; thus establishing the technique as suitable computerized segmentation technique for knee cartilage segmentation.
KW - Knee cartilage
KW - Osteoarthritis
KW - Random walks
KW - Semi-automated
UR - http://www.scopus.com/inward/record.url?scp=85018191606&partnerID=8YFLogxK
U2 - 10.1145/3051166.3051169
DO - 10.1145/3051166.3051169
M3 - Conference Proceeding
AN - SCOPUS:85018191606
T3 - ACM International Conference Proceeding Series
SP - 12
EP - 16
BT - Proceedings of the 7th International Conference on Bioscience, Biochemistry and Bioinformatics, ICBBB 2017
PB - Association for Computing Machinery
Y2 - 21 January 2017 through 23 January 2017
ER -