Literature DB >> 2924283

Skin cancer recognition by computer vision.

R H Moss1, W V Stoecker, S J Lin, S Muruganandhan, K F Chu, K M Poneleit, C D Mitchell.   

Abstract

Automatic detection of several features characteristic of basal cell epitheliomas is described. The features selected for this feasibility study are semitranslucency, telangiectasia, ulcer, crust, and tumor border. Image processing methods used in this study include frequency analysis of the Fourier transform of the image, the Sun-Wee texture analysis algorithm, and several other image analysis techniques suitable for skin photographs. This image analysis software is designed for use with AI/DERM, an expert system that models diagnosis of skin tumors by dermatologists.

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Year:  1989        PMID: 2924283     DOI: 10.1016/0895-6111(89)90076-1

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  4 in total

1.  A relative color approach to color discrimination for malignant melanoma detection in dermoscopy images.

Authors:  R Joe Stanley; William V Stoecker; Randy H Moss
Journal:  Skin Res Technol       Date:  2007-02       Impact factor: 2.365

2.  A systematic heuristic approach for feature selection for melanoma discrimination using clinical images.

Authors:  Ying Chang; R Joe Stanley; Randy H Moss; William Van Stoecker
Journal:  Skin Res Technol       Date:  2005-08       Impact factor: 2.365

3.  Colour analysis of skin lesion regions for melanoma discrimination in clinical images.

Authors:  Jixiang Chen; R Joe Stanley; Randy H Moss; William Van Stoecker
Journal:  Skin Res Technol       Date:  2003-05       Impact factor: 2.365

4.  Colour histogram analysis for melanoma discrimination in clinical images.

Authors:  Yunus Faziloglu; R Joe Stanley; Randy H Moss; William Van Stoecker; Rob P McLean
Journal:  Skin Res Technol       Date:  2003-05       Impact factor: 2.365

  4 in total

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