A significant number of malignant melanomas, especially early melanomas curable by excision, are not diagnosed correctly in the clinic. Available teaching materials do not allow recognition of the critical features of melanoma, especially for early lesions. The Phase I digital image analysis effort has resulted in a set of clinical rules that may permit higher accuracy in diagnosis of early melanomas. Digital image analysis will be continued in Phase II to improve diagnostic accuracy. The Melanoma Detection CD-ROM will be developed for advanced medical students and primary care physicians. The tutorial will be tested and modified by both target groups prior to general release. The CD-ROM will incorporate some new computer instruction techniques and an atlas with descriptor and thumbnail image indexing capability, to allow """"""""best- match"""""""" lookup. This CD-ROM will utilize results available only recently measuring diagnostic performance based on image features, successive pairs in discrimination, and diagnosis of atypical melanomas. The success of the module will be measured using medical student diagnostic performance with slides and primary care clinical outcomes. The semi- automatic classification system from Phase I will be modified and tested on the larger set of images available in Phase II using ROC curves.

Proposed Commercial Applications

Specific features of melanoma will be presented quantitatively to medical students and primary care physicians in a CD-ROM tutorial. No similar product exists, and sales are expected to be significant. The semi- automated diagnostic system will have add-on potential for digital dermatoscopic systems.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
Project #
2R44CA060294-02A2
Application #
6015544
Study Section
Special Emphasis Panel (ZRG1-SSS-7 (75))
Program Officer
Kagan, Jacob
Project Start
1993-08-01
Project End
2001-07-31
Budget Start
1999-08-04
Budget End
2000-07-31
Support Year
2
Fiscal Year
1999
Total Cost
Indirect Cost
Name
Stoecker & Associates
Department
Type
DUNS #
City
Rolla
State
MO
Country
United States
Zip Code
65401
Cheng, Beibei; Joe Stanley, R; Stoecker, William V et al. (2013) Automatic dirt trail analysis in dermoscopy images. Skin Res Technol 19:e20-6
Wang, Hanzheng; Moss, Randy H; Chen, Xiaohe et al. (2011) Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images. Comput Med Imaging Graph 35:116-20
Dalal, Ankur; Moss, Randy H; Stanley, R Joe et al. (2011) Concentric decile segmentation of white and hypopigmented areas in dermoscopy images of skin lesions allows discrimination of malignant melanoma. Comput Med Imaging Graph 35:148-54
Stoecker, William V; Wronkiewiecz, Mark; Chowdhury, Raeed et al. (2011) Detection of granularity in dermoscopy images of malignant melanoma using color and texture features. Comput Med Imaging Graph 35:144-7
Wang, Hanzheng; Chen, Xiaohe; Moss, Randy H et al. (2010) Watershed segmentation of dermoscopy images using a watershed technique. Skin Res Technol 16:378-84
Stoecker, William V; Kolm, Isabel; Rabinovitz, Harold S et al. (2009) Semitranslucency in dermoscopic images of basal cell carcinoma. Arch Dermatol 145:224
Celebi, M Emre; Schaefer, Gerald; Iyatomi, Hitoshi et al. (2009) An improved objective evaluation measure for border detection in dermoscopy images. Skin Res Technol 15:444-50
Xu, Jin; Gupta, Kapil; Stoecker, William V et al. (2009) Analysis of globule types in malignant melanoma. Arch Dermatol 145:1245-51
Guo, L; Umbaugh, S; Cheng, Y (2001) Compression of color skin tumor images with vector quantization. IEEE Eng Med Biol Mag 20:152-64