Numerical characterisation of biomedical titanium surface texture using novel feature parameters

Abstract

In biomedicine, titanium materials with specific surface textures (for example, those produced using consecutive polishing, sandblasting and acid etching) are widely used to facilitate osseointegration of human/animal tissue with implant surface structures. The surface texture has a critical role on the material"s functionality in terms of cellular adhesion and proliferation. However, conventional surface topography characterisation parameters pertinent to cellular attachment such as Sds (density of summits of a surface) and Ssc (arithmetic mean summit curvature of a surface) are liable to be influenced by low amplitude high spatial frequency components or measurement noise. In this research, a novel feature characterisation, based on pattern recognition techniques is implemented on specially processed titanium surfaces. The mean dimensions and densities of the micro-scale features of the surfaces are extracted. The statistical analysis results demonstrate the efficiency and stability of the feature analysis compared to the use of conventional surface texture parameters. Additionally, potentially efficient parameters for characterisation of biomedical surfaces are indicated through the use of a one-way analysis of variance. As a case study, from the point of view of surface metrology, limited experimental data is presented; the intention of the authors is to give a guide to innovative use of the novel surface characterisation techniques. A large amount of biomedical experiments would be needed in the future to fully validate the correlation between the surface texture parameters and its biomedical functions but the present work provides a useful start point for a larger study.

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

“…This group of parameters uses novel pattern recognition techniques which ensure that targeted surface features could be examined verisimilarly [41][42][43]. In addition, from material and tribological aspects, the feature parameters are normally considered being highly functional relevance and stable to target surface [41,55]. The above merits of the feature parameters have been confirmed in this study by the ANOVA results demonstrating that 8 of the total 9 feature parameters have significance with the wear debris surface modification under the wear progression.…”

“…Innovatively defined feature parameters apply statistics to a subset of predefined topographic features which are usually extracted using the pattern recognition techniques such as the "hill and valley detection" [41], "edge detection" [54] and "Wolf pruning" [43]. Some feature parameter applications in engineering disciplines have shown their significant advantageous [41,54,55]. These two parameter sets with a total of thirty-two numerical parameters have been applied to analyse the wear particles using the SURFSTAND [44].…”

Functional Importance of Surface Texture Parameters

“…There are the following feature parameters [11]: The feature-based characteristion technique was used in various areas, such as tribology [194,195], machining [196,197], and biomedicine [198]. Hao et al [194] applied it to the analysis of disc and ball surfaces after tribological tests.…”

“…Feature characterisation was used for surfaces of electroplated diamond tools [ 197 ]. Wang et al [ 198 ] characterised biomedical titanium surfaces by feature parameters. The watershed segmentation method is also suitable for analysis of additively manufactured freeform surfaces [ 199 ].…”

Detection of Single Nucleotide Polymorphisms Using a Biosensor-Containing Titanium-Well Array

“…Ti is highly biocompatible and has previously been used in dental and orthopedic transplants. [11][12][13] The performance of our highly sensitive biocompatible microarray SNP sensor was characterized using a fluorescence scanner.…”