TY - JOUR
T1 - Comparison of improved semi-automated segmentation technique with manual segmentation
T2 - Data from the osteoarthritis initiative
AU - Gan, Hong Seng
AU - Sayuti, Khairil Amir
N1 - Publisher Copyright:
© 2016 Hong-Seng, Gan and Khairil Amir Sayuti.
PY - 2016/10/24
Y1 - 2016/10/24
N2 - Manual segmentation is the standard procedure in osteoarthritis study. However, this method is infamous for being excessive, time consuming and exhaustive. In this study, we overcame the problem of excessive expert interaction reported in manual segmentation by developing a semi-automated random walks technique with computer-aided labelling system. To minimize expert interaction, non-cartilage seeds were generated by using computer. Then, random walks algorithm would segment knee cartilage based on cartilage seeds and non-cartilage seeds. Finally, segmentation results were revised and refined accordingly. A total of 15 normal images and 10 osteoarthritic images were used in this study. In term of efficiency, we have reduced the processing time to segment normal cartilage by 47.5% (93±21s; P = 0.0000019) for observer 1 and 44% (61±8s; P = 3.52×10−5) for observer 2. We also reduced the processing time to segment diseased cartilage by 48.1% (56±16s; P = 0.00014) for observer 1 and 30.3% (62±14s; P = 0.0070) for observer 2. Besides, the proposed technique have produced good reproducibility in both normal (0.83±0.028 for observer 1 and 0.80±0.040 for observer 2) and diseased (0.80±0.060 for observer 1 and 0.82±0.043 for observer 2) cartilage segmentations. In conclusion, the combination of computer generated seeds and user-friendly random walks method have reduced the amount of expert interaction to necessary level without compromising the accuracy of results.
AB - Manual segmentation is the standard procedure in osteoarthritis study. However, this method is infamous for being excessive, time consuming and exhaustive. In this study, we overcame the problem of excessive expert interaction reported in manual segmentation by developing a semi-automated random walks technique with computer-aided labelling system. To minimize expert interaction, non-cartilage seeds were generated by using computer. Then, random walks algorithm would segment knee cartilage based on cartilage seeds and non-cartilage seeds. Finally, segmentation results were revised and refined accordingly. A total of 15 normal images and 10 osteoarthritic images were used in this study. In term of efficiency, we have reduced the processing time to segment normal cartilage by 47.5% (93±21s; P = 0.0000019) for observer 1 and 44% (61±8s; P = 3.52×10−5) for observer 2. We also reduced the processing time to segment diseased cartilage by 48.1% (56±16s; P = 0.00014) for observer 1 and 30.3% (62±14s; P = 0.0070) for observer 2. Besides, the proposed technique have produced good reproducibility in both normal (0.83±0.028 for observer 1 and 0.80±0.040 for observer 2) and diseased (0.80±0.060 for observer 1 and 0.82±0.043 for observer 2) cartilage segmentations. In conclusion, the combination of computer generated seeds and user-friendly random walks method have reduced the amount of expert interaction to necessary level without compromising the accuracy of results.
KW - Knee cartilage
KW - MR image
KW - Osteoarthritis
KW - Seeds
KW - Semi-automated segmentation
UR - http://www.scopus.com/inward/record.url?scp=85004074740&partnerID=8YFLogxK
U2 - 10.3844/ajassp.2016.1068.1075
DO - 10.3844/ajassp.2016.1068.1075
M3 - Article
AN - SCOPUS:85004074740
SN - 1546-9239
VL - 13
SP - 1068
EP - 1075
JO - American Journal of Applied Sciences
JF - American Journal of Applied Sciences
IS - 11
ER -