Bidirectional imaging and modeling of skin texture

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....

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2004-12, Vol.51 (12), p.2148-2159
Main Authors: Cula, O.G., Dana, K.J., Murphy, F.P., Rao, B.K.
Format: Article
Language:English
Subjects:
3-D texture
Algorithms
Appearance-based modeling
Artificial Intelligence
Automatic control
bidirectional texture function
Biomedical imaging
BTF
Cameras
Databases, Factual
Digital images
Humans
Image databases
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Lighting
Medical diagnostic imaging
Models, Biological
Pattern Recognition, Automated - methods
Protocols
Skin
Skin - pathology
Skin - physiopathology
Skin Diseases - pathology
Skin Diseases - physiopathology
skin texture
Telemedicine
texture
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2004.836520