Measurement and image processing evaluation of surface modifications of dental implants G4 pure titanium created by different techniques

Foreign substances and organic tissue interaction placed into the jaw in order to eliminate tooth loss involves a highly complex process. Many biological reactions take place as well as the biomechanical forces that influence this formation. Osseointegration denotes to the direct structural and functional association between the living bone and the load-bearing artificial implant's surface. Taking into consideration of the requirements in the manufacturing processes of the implants, surface characterizations with high precise measurement techniques are investigated and thus long-term success of dental implant is emphasized on the importance of these processes in this study. In this research, the detailed surface characterization was performed to identify the dependence of the manufacturing techniques on the surface properties by using the image processing methods and using the scanning electron microscope (SEM) for morphological properties in 3D and Taylor Hobson stylus profilometer for roughness properties in 2D. Three implant surfaces fabricated by different manufacturing techniques were inspected, and a machined surface was included into the study as a reference specimen. The results indicated that different surface treatments were strongly influenced surface morphology. Thus 2D and 3D precise inspection techniques were highlighted on the importance for surface characterization. Different image analyses techniques such as Dark-light technique were used to verify the surface measurement results. The computational phase was performed using image processing toolbox in Matlab with precise evaluation of the roughness for the implant surfaces. The relationship between the number of black and white pixels and surface roughness is presented. FFT image processing and analyses results explicitly imply that the technique is useful in the determination of surface roughness. The results showed that the number of black pixels in the image increases with increase in surface roughness.