Quantitative Morphometry for Osteochondral Tissues Using Second Harmonic Generation Microscopy and Image Texture Information

Abstract Osteoarthritis (OA) is a chronic joint disorder involving degeneration of articular cartilage and subchondral bone in joints. We previously established a second harmonic generation (SHG) imaging technique for evaluating degenerative changes to articular cartilage in an OA mouse model. SHG i...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Takashi Saitou, Hiroshi Kiyomatsu, Takeshi Imamura
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2018
Materias:
R
Q
Acceso en línea:https://doaj.org/article/90dbae1df58543c1a462d5489c64bc05
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario:Abstract Osteoarthritis (OA) is a chronic joint disorder involving degeneration of articular cartilage and subchondral bone in joints. We previously established a second harmonic generation (SHG) imaging technique for evaluating degenerative changes to articular cartilage in an OA mouse model. SHG imaging, an optical label-free technique, enabled observation of collagen fibrils, and characterized critical changes in the collagenous patterns of the joints. However, it still remains to be determined how morphological changes in the organization of tissue collagen fibrils should be quantified. In this study, we addressed this issue by employing an approach based on texture analysis. Image texture analysis using the gray level co-occurrence matrix was explored to extract image features. We investigated an image patch-based strategy, in which texture features were extracted on individual patches derived from original images to capture local structural patterns in them. We verified that this analysis enables discrimination of cartilaginous and osseous tissues in mouse joints. Moreover, we applied this method to OA cartilage pathology assessment, and observed improvements in the performance results compared with those obtained using an existing feature descriptor. The proposed approach can be applied to a wide range of conditions associated with collagen remodeling and diseases of cartilage and bone.