Modeling of Articular Cartilage with Goal of Early Osteoarthritis Extraction Based on Local Fuzzy Thresholding Driven by Fuzzy C-Means Clustering

• Published in: Intelligent Information and Database Systems pp. 289 - 299
• Main Authors: Kubicek, Jan, Krestanova, Alice, Penhaker, Marek, Augustynek, Martin, Cerny, Martin, Oczka, David
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Articular cartilage; FCM; Image segmentation; Local thresholding
• ISBN: 9783030148010; 3030148017
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-14802-7_25

One of the routine tasks in the Orthopedics practice is the articular cartilage assessment. Proper cartilage assessment includes a precise localization, and recognition of spots indicating the cartilage loss caused by the osteoarthritis. Unfortunately, such tasks are performed manually, without the SW feedback, which leads to various clinical outputs based on the physician’s experience. Based on such facts, a development of the fully automatic systems bringing automatic modeling and classification of the cartilage is clinically very important. In our paper we have proposed a local thresholding multiregional segmentation method for the cartilage segmentation from the MR (Magnetic Resonance) images. In our approach, an optimal configuration of the fuzzy triangular sets is driven by the FCM clustering to obtain an optimal segmentation model based on the thresholding. We have verified the proposed model on a sample of the 200 MR image records containing the early osteoarthritis signs.