In this work, we present a method of skin imaging called bidirectional
imaging that captures significantly more properties of appearance than
standard imaging. The observed structure of the skin's surface is greatly
dependent on the angle of incident illumination and the angle of
observation. Specific protocols to achieve bidirectional imaging are
presented and used to create the Rutgers Skin Texture Database (clinical
component). This image database is the first of its kind in the dermatology
community. Skin images of several disorders under multiple controlled
illumination and viewing directions are provided publicly for research and
educational use. Using this skin texture database, we employ computational
surface modeling to perform automated skin texture classification. The
classification experiments demonstrate the usefulness of the modeling and
measurement methods.
IEEE Transactions on Biomedical Engineering, Vol. 51: No. 12, pp. 2148-2159, December 2004 (submitted September 2003, submitted revised February 2004, submitted camera-ready May 2004).
This material is based upon work supported by the National Science Foundation under Grant No. 0092491 and Grant No. 0085864.