Peningkatan Akurasi Segmentasi Tulang Femur dan Tibia pada Citra Radiograf Menggunakan AASM

Rima Tri Wahyuningrum, I Ketut Eddy Purnama, Mauridhi Hery Purnomo


Osteoarthritis (OA) is a joint disease that affects a large part of the elderly population. One of the OA that is often experienced by patients is knee OA. To determine the development and classification of this disease, a process of segmenting the femur and tibia is needed quickly and accurately. Meanwhile, manual segmentation has several disadvantages including the longer time needed and the difference in the results of reading x-ray images between medical personnel with each other. Therefore, in this paper, an Adaptive Active Shape Model (AASM) is presented for femur and tibia segmentation on knee x-ray images. The purpose of this segmentation is to support the discovery and characterization of imaging biomarkers for the incidence, clinical evaluation, classification, and progression of knee osteoarthritis (OA). This new algorithm is adaptively capable of better segmenting the femur and tibia than the original ASM. In this experiment, 10 images were used as training data to get the mean shape model and 50 images were tested to find out performance of the method implemented. All images are taken randomly from Osteoarthritis Initiative (OAI) dataset. To determinate the accuracy of this segmentation method, calculations have been performed using Hausdorff Distance (HD) and Dice Similarity Coefficient (DSC). In addition, this study have also been compared with previous research (original ASM) and the same data is used. The best average result of the segmentation validation method from 50 test images in the AASM method using HD is 0.2016 for the right tibia femur bone using 43 landmarks and 0.9497 for the DSC. Based on these results, the average increase in accuracy of segmentation validation was 0.29 for HD and 0.33 for DSC. Thus, this method is quite reliable and clinically valuable for monitoring the progression of knee osteoarthritis.


osteoartritis lutut; segmentasi; Active Shape Model; Hausdorff Distance; Dice Similarity Coefficient

Full Text:



A.W. Sudoyo, B. Setiyohadi, I. Alwi, M. Simadibrata, dan S. Setiati, “Buku Ajar Ilmu Penyakit Dalam Jilid II Edisi V”, Jakarta, Indonesia: Interna Publishing, 2009.

(2018) “Chronic rheumatic conditions” [Online],, tanggal akses: 22 Mei 2018.

Rekomendasi IRA untuk Diagnosis dan Penatalaksanaan Osteoartritis, Diagnosis dan Penatalaksanaan Osteoartritis, 2014.

D. Hayashi, F.W. Roemer, dan A. Guermazi, “Review: Imaging for osteoarthritis,” Annals of Physical and Rehabilitation Medicine, Vol. 59, hal. 161-169, 2016.

W.M. Oo, J.M. Linklater, dan D.J. Hunter, “Imaging in Knee Osteoarthritis”, Therapy, Vol. 7, No. 6, hal. 635–647, 2010.

L. Shamir, S.M. Ling, W.W. Scott, A. Bos, N. Orlov, T.J. Macura, D.M. Eckley, L. Ferrucci, dan I.G. Goldberg, “Knee X-Ray Image Analysis Method for Automated Detection of Osteoarthritis,” IEEE Transaction on Biomedical Engineering, Vol. 56, No. 2, hal. 407–415, 2009.

G.W. Stachowiak, M. Wolski, T. Woloszynski, dan P. Podsiadlo, “Detection and Prediction of Osteoarthritis in Knee and Hand Joints Based on the X-Ray Image Analysis,” Biosurface and Biotribology, Vol. 2, No. 4, hal. 162-172, Dec 2016.

N. Hafezi-Nejad, A. Guermazi, S. Demehri, dan F.W. Roemer, ”New Imaging Modalities to Predict and Evaluate Osteoarthritis Progression”, Best Practice & Research Clinical Rheumatology, Vol. 31, No. 5, hal. 688-704, Okt. 2017.

(2016) Laman Osteoarthritis Initiative [Online],, tanggal akses: 15-Jan-2016.

R.T. Wahyuningrum, L. Anifah, I.K.E. Purnama, dan M.H. Purnomo, “A Novel Hybrid of S2DPCA and SVM for Knee Osteoarthritis Classification,” 2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measuring Systems and Applications (CIVEMSA 2016), 2016, hal. 57-61.

R. Riad, R. Jennane, A. Brahim, T. Janvier, H. Toumi, dan E. Lespessailles, “Texture Analysis Using Complex Wavelet Decomposition for Knee Osteoarthritis Detection: Data from the Osteoarthritis Initiative,” Computers and Electrical Engineering, Vol. 68, hal. 181–191, 2018.

J.A. Lynch, N. Parimi, R.K. Chaganti, M.C. Nevitt, dan N.E. Lane, “The Association of Proximal Femoral Shape and Incident Radiographic Hip OA in Elderly Women,” Osteoarthritis and Cartilage, Vol. 17, No. 10, hal. 1313–1318, 2009.

C. Chen, W. Xie, J. Franke, P.A. Grutzner, L.–P. Nolte, dan G. Zheng, “Automatic X-Ray Landmark Detection and Shape Segmentation Via Data-Driven Joint Estimation of Image Displacement,” Medical Image Analysis, Vol. 18, No. 3, hal. 487-499, 2014.

B.L. Wise, L. Kritikos, J.A. Lynch, F. Liu, N. Parimi, K.L. Tileston, dan M.C. Nevitt, “Proximal Femur Shape Differs between Subjects with Lateral and Medial Knee Osteoarthritis and Controls: The Osteoarthritis Initiative,” Osteoarthritis and Cartilage, Vol.22, No. 12, hal. 2067-2073, 2014.

B.L.Wise, F. Liu, L. Kritikos, J.A. Lynch, N. Parimi, Y. Zhang, dan N.E. Lane, “The Association of Distal Femur and Proximal Tibia Shape with Sex: The Osteoarthritis Initiative,” Seminars in Arthritis and Rheumatism, Vol. 46, No. 1, hal. 20–26, 2016.

A. Gandhamal, S. Talbar, S. Gajre, R. Razak, A.F.M. Hani, dan D. Kumar, “Fully Automated Subchondral Bone Segmentation from Knee MR Images: Data from the Osteoarthritis Initiative,” Computers in Biology and Medicine, Vol. 88, hal. 110-125, 2017.

D.A. Dharmawan, “Deteksi Kanker Serviks Otomatis Berbasis Jaringan Saraf Tiruan LVQ dan DCT”, Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), Vol. 6, No. 1, hal. 107-114, 2017.

N.P. Husain dan C. Fatichah, “Segmentasi Citra Sel Tunggal Smear Serviks Menggunakan Radiating Component Normalized Generalized GVFS,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), Vol. 6, No. 1, hal. 107-114, 2017.

N. Syakrani, Y. Widhiyasana, dan A.A. Efendi, “Deteksi Tumor Hati dengan Graph Cut dan Taksiran Volume Tumornya,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), Vol. 7, No. 1, hal. 35-43, 2018.

K.I. Pangestuti dan I.K.E. Purnama, “Pengukuran Sudut Tibia dan Femur pada Citra X-Ray Menggunakan Active Shape Model (ASM)”, Prosiding Seminar Nasional Pendidikan Teknik Informatika (SENAPATI 2015), 2015, hal.134-139.

M. Esfandiarkhani dan A.H. Foruzan, “A Generalized Active Shape Model for Segmentation of Liver in Low-Contrast CT Volumes”, Computers in Biology and Medicine, Vol. 82, hal. 59-70, 2017.

X. Chen, J.K. Udupa, A. Alavi, dan D.A. Torigian, “GC-ASM: Synergistic Integration of Graph-Cut and Active Shape Model Strategies for Medical Image Segmentation,” Computer Vision and Image Understanding, Vol. 117, No. 5, hal. 513–524, 2013.

C. Lindner, S. Thiagarajah, J.M. Wilkinson, The arcOGEN Consortium, G.A. Wallis dan T.F. Cootes, “Fully Automatic Segmentation of the Proximal Femur Using Random Forest Regression Voting,” IEEE Trans. Med. Imaging, Vol. 32, No. 8, hal.1462–1472, 2013.

J. Mu, X. Liu, S. Luan, P.H. Heintz, G.W. Mlady, dan D.Z. Chen, “Segmentation of Knee Joints in X-Ray Images Using Decomposition-Based Sweeping and Graph Search,” Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 2011, hal. 1-8.

T.F. Cootes dan C.J. Taylor, “Statistical Models of Appearance for Computer Vision,” University of Manchester, Manchester, United Kingdom, Technical Report, hal. 1-125, 2000.

A.A Taha dan A. Hanbury, “An Efficient Algorithm for Calculating the Exact Hausdorff Distance,” IEEE Trans Pattern Anal Mach Intell, Vol. 37, No. 11, hal. 2153–2163, 2015.

K.H. Zou, S.K. Warfield, A. Bharatha, C.M. C. Tempany, M.R. Kaus, S. J. Haker, W.M. Wells, F.A. Jolesz, dan R. Kikinis, “Statistical Validation of Image Segmentation Quality Based on a Spatial Overlap Index—Scientific Reports,” Acad. Radiol., Vol. 11, No. 2, hal. 178–189, 2004.



  • There are currently no refbacks.

Copyright (c) 2019 Jurnal Nasional Teknik Elektro dan Teknologi Informasi

JNTETI (Jurnal Nasional Teknik Elektro dan Teknologi Informasi)

Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik Universitas Gadjah Mada
Jl. Grafika No 2. Kampus UGM Yogyakarta 55281
+62 274 552305