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

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