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. 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.

[1]  J Smolle,et al.  Terminology in surface microscopy. Consensus meeting of the Committee on Analytical Morphology of the Arbeitsgemeinschaft Dermatologische Forschung, Hamburg, Federal Republic of Germany, Nov. 17, 1989. , 1990, Journal of the American Academy of Dermatology.

[2]  M. Chantler.  Why illuminant direction is fundamental to texture analysis , 2022 .

[3]  Glenn Healey,et al.  The Analysis and Recognition of Real-World Textures in Three Dimensions , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Harald Ganster,et al.  Automated Melanoma Recognition , 2001, IEEE Trans. Medical Imaging.

[5]  Shree K. Nayar,et al.  Correlation model for 3D texture , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[6]  Joachim M. Buhmann,et al.  Histogram clustering for unsupervised image segmentation , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[7]  P Bauer,et al.  Dermatoscopy: usefulness in the differential diagnosis of cutaneous pigmentary lesions , 1994, Melanoma research.

[8]  Robert M. Haralick,et al.  Graph-theoretic clustering for image grouping and retrieval , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[9]  Shree K. Nayar,et al.  Reflectance and texture of real-world surfaces , 1999, TOGS.

[10]  Wilson S. Geisler,et al.  Multichannel Texture Analysis Using Localized Spatial Filters , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Anil K. Jain,et al.  A Multichannel Approach to Fingerprint Classification , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  K Wolff,et al.  Statistical evaluation of epiluminescence microscopy criteria in the differential diagnosis of malignant melanoma and pigmented basal cell carcinoma , 1997, Melanoma research.

[13]  W C Lambert,et al.  Melanoma diagnosis by computerized analysis of clinical images. , 2001, Archives of dermatology.

[14]  Kristin J. Dana,et al.  Texture histograms as a function of irradiation and viewing direction , 1999, International Journal of Computer Vision.

[15]  DongJunyu,et al.  Capture and Synthesis of 3D Surface Texture , 2005 .

[16]  Hiroshi Murase,et al.  Visual learning and recognition of 3-d objects from appearance , 2005, International Journal of Computer Vision.

[17]  M. Chantler,et al.  Capture and Synthesis of 3D Surface Texture , 2004, International Journal of Computer Vision.

[18]  G Pellacani,et al.  Digital videomicroscopy improves diagnostic accuracy for melanoma. , 1998, Journal of the American Academy of Dermatology.

[19]  Kristin J. Dana,et al.  3D Texture Recognition Using Bidirectional Feature Histograms , 2004, International Journal of Computer Vision.

[20]  A. Green,et al.  Computer image analysis of pigmented skin lesions , 1991, Melanoma research.

[21]  A. Green,et al.  Computer image analysis in the diagnosis of melanoma. , 1994, Journal of the American Academy of Dermatology.

[22]  Andrew Zisserman,et al.  Classifying Images of Materials: Achieving Viewpoint and Illumination Independence , 2002, ECCV.

[23]  Jun Zhang,et al.  Analysis of the network pattern in dermatoscopic images , 1999 .

[24]  K Wolff,et al.  In vivo epiluminescence microscopy of pigmented skin lesions. II. Diagnosis of small pigmented skin lesions and early detection of malignant melanoma. , 1987, Journal of the American Academy of Dermatology.

[25]  L Goldman,et al.  Direct microscopy of skin in vivo as a diagnostic aid and research tool. , 1980, The Journal of dermatologic surgery and oncology.

[26]  Kristin J. Dana,et al.  Skin Texture Modeling , 2005, International Journal of Computer Vision.

[27]  R. Kenet,et al.  Clinical diagnosis of pigmented lesions using digital epiluminescence microscopy. Grading protocol and atlas. , 1993, Archives of dermatology.

[28]  B. Julesz.  Textons, the elements of texture perception, and their interactions , 1981, Nature.

[29]  E Claridge,et al.  Shape analysis for classification of malignant melanoma. , 1992, Journal of biomedical engineering.

[30]  Kristin J. Dana,et al.  Compact representation of bidirectional texture functions , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[31]  Kristin J. Dana,et al.  Recognition methods for 3D textured surfaces , 2001, IS&T/SPIE Electronic Imaging.

[32]  Jan J. Koenderink,et al.  Bidirectional Texture Contrast Function , 2002, ECCV.

[33]  L GOLDMAN,et al.  Some investigative studies of pigmented nevi with cutaneous microscopy. , 1951, The Journal of investigative dermatology.

[34]  Jitendra Malik,et al.  Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.

[35]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  N Cascinelli,et al.  A possible new tool for clinical diagnosis of melanoma: the computer. , 1987, Journal of the American Academy of Dermatology.

[37]  W Abmayr,et al.  Improvement of monitoring of melanocytic skin lesions with the use of a computerized acquisition and surveillance unit with a skin surface microscopic television camera. , 1996, Journal of the American Academy of Dermatology.

[38]  J. Koenderink,et al.  Bidirectional Texture Contrast Function , 2004, International Journal of Computer Vision.

[39]  B. S. Manjunath,et al.  Texture features and learning similarity , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[40]  W Abmayr,et al.  Evaluation of different image acquisition techniques for a computer vision system in the diagnosis of malignant melanoma. , 1994, Journal of the American Academy of Dermatology.

[41]  Shree K. Nayar,et al.  Histogram model for 3D textures , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[42]  Kristin J. Dana,et al.  Image-based Skin Analysis , 2002 .

[43]  K Wolff,et al.  Pigmented Spitz nevi: improvement of the diagnostic accuracy by epiluminescence microscopy. , 1992, Journal of the American Academy of Dermatology.

[44]  J. Bystryn,et al.  Epiluminescence microscopy: a reevaluation of its purpose. , 2001, Archives of dermatology.

[45]  Luc Van Gool,et al.  Multiview texture models , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[46]  K Wolff,et al.  In vivo epiluminescence microscopy of pigmented skin lesions. I. Pattern analysis of pigmented skin lesions. , 1987, Journal of the American Academy of Dermatology.

[47]  Mike J. Chantler,et al.  Rough surface classification using point statistics from photometric stereo , 2000, Pattern Recognit. Lett..

[48]  H. Kittler,et al.  Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network , 1998, Melanoma research.

[49]  Andreas Steiner,et al.  Quantitative evaluation of epiluminescence microscopy criteria of melanocytic pigmented skin lesions: 302 , 1993 .