A new approach to numerical characterisation of wear particle surfaces in three-dimensions for wear study

Abstract

In the wear and tear process of synovial joints, wear particles generated and released from articular cartilage within the joints have different surface topology and mechanical property. Three-dimensional (3D) particle images acquired using laser scanning confocal microscopy (LSCM) contain appropriate surface information for quantitatively characterizing the surface topology and changes to seek a further understanding of the wear process and wear features. This paper presents a new attempt on the 3D numerical characterisation of wear particle surfaces using the field and feature parameter sets which are defined in ISO/FDIS 25178-2. Based on the innovative pattern recognition capability, the feature parameters are, for the first time, employed for quantitative analysis of wear debris surface textures. Through performing parameter classification, ANOVA analysis and correlation analysis, typical changing trends of the surface transformation of the wear particles along with the severity of wear conditions and Osteoarthritis (OA) have been observed. Moreover, the feature parameters have shown a significant sensitivity with the wear particle surfaces texture evolution under OA development. A correlation analysis of the numerical analysis results of cartilage surface texture variations and that of their wear particles has been conducted in this study. Key surface descriptors have been determined. Further research is needed to verify the above outcomes using clinic samples.

Wear in human knees

“…Investigations of the size and shape of individual wear particles are allowed as the particle overlapping problem in the ferrography can be significantly minimized or eliminated. It has been applied to investigate metallic wear particles [92] and ultrahigh molecular weight polyethylene wear particles found in knee prosthesis [93], as well as to wear particles found in sheep knee synovial fluid [39,94]. Caution is needed when applying this technique because the filter membrane may be ruptured due to a large applied force.…”

“…LSCM is able to capture the boundary and surface information of the wear particles at a micrometer scale. LSCM was used to acquire the 3D surface information of sheep wear particles [94].…”

“…A feature parameter is defined from a subset of predefined topographic features from the scale limited surface [60]. These numerical parameters were applied to characterize the matt finish femoral stem surfaces [61], surfaces of hard on hard bearings in prosthetic hip joints, cartilage surface [58], and wear particles found in sheep knee joint [94]. The surface topography of human wear particles may be described using the field and feature parameters defined in ISO 25178-2.…”

“…The surface topographies of wear particles contain information on particle formation [94] and the wear mechanism of the knee joint [4]. Few studies have investigated their surface topographies and their relation to osteoarthritis severity of the human knee joint.…”

Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine

“…Xu et al [9] developed a set of wear particle image analysis system through the integration of neural network and expert system, which can realize the interactive wear particles image automatic recognition. Tian et al [10] and Peng and Kirk [11] employed laser scanning confocal microscopy to acquire three-dimensional particle images. The scanning method provides surface information from the analysis of wear surface morphology, which facilitates better understanding of wear features and wear level.…”