15 May 2014 Knee cartilage segmentation using active shape models and local binary patterns
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Abstract
Segmentation of knee cartilage has been useful for opportune diagnosis and treatment of osteoarthritis (OA). This paper presents a semiautomatic segmentation technique based on Active Shape Models (ASM) combined with Local Binary Patterns (LBP) and its approaches to describe the surrounding texture of femoral cartilage. The proposed technique is tested on a 16-image database of different patients and it is validated through Leave- One-Out method. We compare different segmentation techniques: ASM-LBP, ASM-medianLBP, and ASM proposed by Cootes. The ASM-LBP approaches are tested with different ratios to decide which of them describes the cartilage texture better. The results show that ASM-medianLBP has better performance than ASM-LBP and ASM. Furthermore, we add a routine which improves the robustness versus two principal problems: oversegmentation and initialization.
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Germán González, Boris Escalante-Ramírez, "Knee cartilage segmentation using active shape models and local binary patterns", Proc. SPIE 9138, Optics, Photonics, and Digital Technologies for Multimedia Applications III, 91380K (15 May 2014); doi: 10.1117/12.2054783; https://doi.org/10.1117/12.2054783
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