Yevgeniya Kobrina1, Lassi Rieppo2, Simo Saarakkala3, Jukka S Jurvelin1, Hanna Isaksson4. 1. Department of Applied Physics, University of Eastern Finland, Finland. 2. Department of Applied Physics, University of Eastern Finland, Finland; Department of Clinical Neurophysiology, Kuopio University Hospital, Kuopio, Finland. 3. Department of Diagnostic Radiology, Institute of Diagnostics, University of Oulu, Finland; Department of Medical Technology, Institute of Biomedicine, University of Oulu, Finland. 4. Department of Applied Physics, University of Eastern Finland, Finland; Division of Solid Mechanics, Lund University, Sweden. Electronic address: hanna.isaksson@solid.lth.se.
Abstract
OBJECTIVE: Articular cartilage (AC) exhibits specific zonal structure that follows the organization of collagen network and concentration of tissue constituents. The aim of this study was to investigate the potential of unsupervised clustering analysis applied to Fourier transform infrared (FTIR) microspectroscopy to detect depth-dependent structural and compositional differences in intact AC. METHOD: Seven rabbit and eight bovine intact patellae AC samples were imaged using FTIR microspectroscopy and normalized raw spectra were clustered using the fuzzy C-means algorithm. Differences in mean spectra of clusters were investigated by quantitative estimation of collagen and proteoglycan (PG) contents, as well as by careful visual investigation of locations of spectral changes. RESULTS: Clustering revealed the typical layered structure of AC in both species. However, more distinct clusters were found for rabbit samples, whereas bovine AC showed more complex layered structure. In both species, clustering structure corresponded with that in polarized light microscopic (PLM) images; however, some differences were also observed. Spectral differences between clusters were identified at the same spectral locations for both species. Estimated PG/collagen ratio decreased significantly from superficial to middle or deep zones, which might explain the difference in clustering results compared to PLM. CONCLUSION: FTIR microspectroscopy in combination with cluster analysis allows detailed examination of spatial changes in AC. As far as we know, no previous single technique could reveal a layered structure of AC without any a priori information.
OBJECTIVE:Articular cartilage (AC) exhibits specific zonal structure that follows the organization of collagen network and concentration of tissue constituents. The aim of this study was to investigate the potential of unsupervised clustering analysis applied to Fourier transform infrared (FTIR) microspectroscopy to detect depth-dependent structural and compositional differences in intact AC. METHOD: Seven rabbit and eight bovine intact patellae AC samples were imaged using FTIR microspectroscopy and normalized raw spectra were clustered using the fuzzy C-means algorithm. Differences in mean spectra of clusters were investigated by quantitative estimation of collagen and proteoglycan (PG) contents, as well as by careful visual investigation of locations of spectral changes. RESULTS: Clustering revealed the typical layered structure of AC in both species. However, more distinct clusters were found for rabbit samples, whereas bovine AC showed more complex layered structure. In both species, clustering structure corresponded with that in polarized light microscopic (PLM) images; however, some differences were also observed. Spectral differences between clusters were identified at the same spectral locations for both species. Estimated PG/collagen ratio decreased significantly from superficial to middle or deep zones, which might explain the difference in clustering results compared to PLM. CONCLUSION: FTIR microspectroscopy in combination with cluster analysis allows detailed examination of spatial changes in AC. As far as we know, no previous single technique could reveal a layered structure of AC without any a priori information.
Authors: Arash Hanifi; Xiaohong Bi; Xu Yang; Beril Kavukcuoglu; Ping Chang Lin; Edward DiCarlo; Richard G Spencer; Mathias P G Bostrom; Nancy Pleshko Journal: Am J Sports Med Date: 2012-10-29 Impact factor: 6.202
Authors: Nora T Khanarian; Margaret K Boushell; Jeffrey P Spalazzi; Nancy Pleshko; Adele L Boskey; Helen H Lu Journal: J Bone Miner Res Date: 2014-12 Impact factor: 6.741