Photorealistic modeling of tissue reflectance properties.
OBJECTIVE
For Minimally Invasive Surgery (MIS) procedures, specular highlights constitute important visual cues for gauging tissue deformation as well as perceiving depth and orientation. This paper describes a novel reflectance modeling technique that is particularly suitable for simulating light interaction behavior with mucus-covered tissue surfaces.


METHODS
The complex and largely random tissue-light interaction behavior is modeled with a noise-based approach. In the proposed technique, Perlin noise is used to modulate the shape of specular highlights and imitate the effects of the complex tissue structure on reflected lighting. For efficient execution, the noise texture is generated in pre-processing and stored in an image-based representation, i.e., a reflectance map. At run-time, the graphics hardware is used to attain per-pixel control and achieve realistic tissue appearance.


RESULTS
The reflectance modeling technique has been used to replicate light-tissue reflection in surgical simulation. By comparing the results acquired against those obtained from conventional per-vertex Phong lighting and OpenGL multi-texturing, it is observed that the noise-based approach achieves improved tissue appearance similar to that observed in real procedures. Detailed user evaluation demonstrates the quality and practical value of the technique for increased perception of photorealism.


CONCLUSIONS
The proposed technique presents a practical strategy for surface reflectance modeling that is suitable for real-time interactive surgical simulation. The use of graphics hardware further enhances the practical value of the technique.
Elhelw
Mohamed A
MA
Royal Society/Wolfson Foundation Medical Image Computing Laboratory, Imperial College London, London, UK.
Nicolaou
Marios
M
Chung
Adrian
A
Yang
Guang-Zhong
GZ
D000465:Algorithms; D003196:Computer Graphics; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement; D007090:Image Interpretation, Computer-Assisted; D008027:Light; D019060:Minimally Invasive Surgical Procedures / Q000295:instrumentation*; D008962:Models, Theoretical; D009622:Noise; D010363:Pattern Recognition, Automated; D010783:Photometry / Q000295:instrumentation*; D012815:Signal Processing, Computer-Assisted; D017695:Soft Tissue Injuries / Q000175:diagnosis / Q000601:surgery; D025321:Surgery, Computer-Assisted