Open AccessRoughness based perceptual analysis towards digital skin imaging system with haptic feedback
Author(s) -
Kim K.
Publication year - 2016
Publication title -
skin research and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.521
H-Index - 69
eISSN - 1600-0846
pISSN - 0909-752X
DOI - 10.1111/srt.12270
Subject(s) - computer vision , haptic technology , rendering (computer graphics) , artificial intelligence , computer science , surface finish , surface roughness , perception , materials science , neuroscience , composite material , biology
Background/purpose To examine psoriasis or atopic eczema, analyzing skin roughness by palpation is essential to precisely diagnose skin diseases. However, optical sensor based skin imaging systems do not allow dermatologists to touch skin images. To solve the problem, a new haptic rendering technology that can accurately display skin roughness must be developed. In addition, the rendering algorithm must be able to filter spatial noises created during 2D to 3D image conversion without losing the original roughness on the skin image. In this study, a perceptual way to design a noise filter that will remove spatial noises and in the meantime recover maximized roughness is introduced by understanding human sensitivity on surface roughness. Methods A visuohaptic rendering system that can provide a user with seeing and touching digital skin surface roughness has been developed including a geometric roughness estimation method from a meshed surface. In following, a psychophysical experiment was designed and conducted with 12 human subjects to measure human perception with the developed visual and haptic interfaces to examine surface roughness. Results From the psychophysical experiment, it was found that touch is more sensitive at lower surface roughness, and vice versa. Human perception with both senses, vision and touch, becomes less sensitive to surface distortions as roughness increases. When interact with both channels, visual and haptic interfaces, the performance to detect abnormalities on roughness is greatly improved by sensory integration with the developed visuohaptic rendering system. Conclusion The result can be used as a guideline to design a noise filter that can perceptually remove spatial noises while recover maximized roughness values from a digital skin image obtained by optical sensors. In addition, the result also confirms that the developed visuohaptic rendering system can help dermatologists or skin care professionals examine skin conditions by using vision and touch at the same time.