Determination of Cartilage Thickness in-vivo Using Back Reflection Spectroscopy

Canpolat, Murat; Gümüş, Birce; Özbey-Ünlü, Özlem; Uslu, Serkan; Nüzket, Tunca; Üncü, Yiğit Ali; Söyüncü, Yetgin; Turgut, Çağrı; Üstünel, İ̇smail

doi:10.1109/biyomut.2017.8479108

Cited by 2 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A spectroscopic method was developed to determine cartilage thickness in-vivo in a previous study. It was reported that their method is able to estimate the cartilage thickness with an error of 12.8 ± 9.5% (Canpolat et al, 2017). The chondrocyte morphology determined by the pericellular matrix demonstrates a zonal specific dependency.…”

Section: Introductionmentioning

confidence: 99%

Derinliğe Bağlı Kondrosit Yüzey Alanı İçin Cevap Yüzey Yöntemi

Çiçek

2021

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi

View full text Add to dashboard Cite

Articular cartilage is a low-friction white connective tissue. The only cell type in articular cartilage are chondrocytes. They permit smooth articulation in cartilage. They have insufficient regenerative capacity. Response surface methodology is a very useful tool for the modelling of any processes using polynomials. This study investigated the depth-dependent chondrocyte surface area from bovine articular cartilage. Confocal microscope was utilized to image osteochondral explants. The response surface methodology was used to constitute the predictive regression model to guess chondrocyte area from confocal image of bovine articular cartilage. This methodology was employed to examine the relationships among input variables and response. The response was surface area of chondrocyte while the inputs were perimeter of chondrocyte and depth. The depth-dependent measured and calculated chondrocyte surface area was demonstrated. The response surface model was significant (p=0.001) and adequate for the prediction the chondrocyte surface area since R2 = 0.81. The chondrocyte surface area can be predicted with perimeter and depth by response surface methodology. The implementation of statistical experimental design techniques in image processing can reduce experimental runs and save experimental animals live.

show abstract

Section: Introductionmentioning

confidence: 99%