DocumentCode :
1167155
Title :
Bidirectional imaging and modeling of skin texture
Author :
Cula, Oana G. ; Dana, Kristin J. ; Murphy, Frank P. ; Rao, Babar K.
Author_Institution :
Electr. & Comput. Eng. Dept., Rutgers Univ., Piscataway, NJ, USA
Volume :
51
Issue :
12
fYear :
2004
Firstpage :
2148
Lastpage :
2159
Abstract :
In this paper, 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.
Keywords :
PACS; biomedical optical imaging; image classification; image texture; medical image processing; physiological models; skin; Rutgers Skin Texture Database; automated skin texture classification; bidirectional imaging; dermatology; image database; skin texture modeling; Automatic control; Biomedical imaging; Cameras; Digital images; Image databases; Lighting; Medical diagnostic imaging; Protocols; Skin; Telemedicine; 3-D texture; Appearance-based modeling; BTF; bidirectional texture function; skin texture; texture; Algorithms; Artificial Intelligence; Databases, Factual; Humans; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Models, Biological; Pattern Recognition, Automated; Skin; Skin Diseases;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2004.836520
Filename :
1360034
Link To Document :
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