Digital image analysis for diagnosis of skin tumors
- PMID: 18486019
- DOI: 10.1016/j.sder.2007.12.005
Digital image analysis for diagnosis of skin tumors
Abstract
Between 1987 and 2007, different groups developed digital image analysis systems for the diagnosis of benign and malignant skin tumors. As the result of significant differences in the technical devices, the number, the nature and benign/malignant ratio of included skin tumors, different variables and statistical methods any comparison of these different systems and their results is difficult. For the use and comparison of the diagnostic performance of different digital image analysis systems in the future, some principle basic conditions are required: All used systems should have a standardized recording system and calibration. First, melanocytic and nonmelanocytic lesions should be included for the development of the diagnostic algorithms. Critical analyses of the results should answer the question if in future only melanocytic lesions should be analyzed or all pigmented and nonpigmented lesions. This will also lead to the answer if only dermatologists or all specialities of medical doctors will use such a system. All artifacts (eg, hairs, air bubbles) should be removed. The number of variables should be chosen according to the number of included melanomas. A high number of benign skin lesions should be included. Of all lesions only 10% or better less should be invasive melanomas. Each system should be developed by a training-set and controlled by an independent test-set. Each system should be controlled by the user with the final decision and responsibility and tested by independent users without any conflict of financial interest.
Similar articles
-
Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions.Br J Dermatol. 2004 Nov;151(5):1029-38. doi: 10.1111/j.1365-2133.2004.06210.x. Br J Dermatol. 2004. PMID: 15541081 Review.
-
The performance of SolarScan: an automated dermoscopy image analysis instrument for the diagnosis of primary melanoma.Arch Dermatol. 2005 Nov;141(11):1388-96. doi: 10.1001/archderm.141.11.1388. Arch Dermatol. 2005. PMID: 16301386
-
A simple digital image processing system to aid in melanoma diagnosis in an everyday melanocytic skin lesion unit: a preliminary report.Int J Dermatol. 2006 Apr;45(4):402-10. doi: 10.1111/j.1365-4632.2006.02726.x. Int J Dermatol. 2006. PMID: 16650167
-
Digital computer analysis of dermatoscopical images of 260 melanocytic skin lesions; perimeter/area ratio for the differentiation between malignant melanomas and melanocytic nevi.J Eur Acad Dermatol Venereol. 2007 Jan;21(1):48-55. doi: 10.1111/j.1468-3083.2006.01864.x. J Eur Acad Dermatol Venereol. 2007. PMID: 17207167
-
Overview of advanced computer vision systems for skin lesions characterization.IEEE Trans Inf Technol Biomed. 2009 Sep;13(5):721-33. doi: 10.1109/TITB.2009.2017529. Epub 2009 Mar 16. IEEE Trans Inf Technol Biomed. 2009. PMID: 19304487 Review.
Cited by
-
Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review.Diagnostics (Basel). 2021 Jul 31;11(8):1390. doi: 10.3390/diagnostics11081390. Diagnostics (Basel). 2021. PMID: 34441324 Free PMC article. Review.
-
Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms.Int J Biomed Imaging. 2013;2013:323268. doi: 10.1155/2013/323268. Epub 2013 Dec 23. Int J Biomed Imaging. 2013. PMID: 24575126 Free PMC article. Review.
-
Computer-Aided Decision Support for Melanoma Detection Applied on Melanocytic and Nonmelanocytic Skin Lesions: A Comparison of Two Systems Based on Automatic Analysis of Dermoscopic Images.Biomed Res Int. 2015;2015:579282. doi: 10.1155/2015/579282. Epub 2015 Nov 26. Biomed Res Int. 2015. PMID: 26693486 Free PMC article.
-
Computer-aided diagnosis of skin lesions using conventional digital photography: a reliability and feasibility study.PLoS One. 2013 Nov 4;8(11):e76212. doi: 10.1371/journal.pone.0076212. eCollection 2013. PLoS One. 2013. PMID: 24223698 Free PMC article.
-
Histology image analysis for carcinoma detection and grading.Comput Methods Programs Biomed. 2012 Sep;107(3):538-56. doi: 10.1016/j.cmpb.2011.12.007. Epub 2012 Mar 20. Comput Methods Programs Biomed. 2012. PMID: 22436890 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical
Research Materials