+ Site Statistics
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Translate
+ Recently Requested

Application of computerized image analysis in pigmentary skin diseases

Application of computerized image analysis in pigmentary skin diseases

International Journal of Dermatology 40(1): 45-49

Melanocyte number and the amount of melanin pigment are related to diagnosis and treatment of pigmentary skin diseases. Various histologic methods are used, such as Fontana-Masson stain for melanin pigment or immunohistochemical stain for melanocytes. Recently, computerized image analysis has been applied to many fields to avoid interobserver bias. In this study, we applied a computerized image analysis to assess the melanin content and melanocyte density of human epidermis. We evaluated the skin biopsy specimens (paraffin blocks) from normal human skin (33 +/- 6.6, n = 11) and diseased skins; vitiligo (32 +/- 10.0, n = 8), melasma (35 +/- 8.6, n = 11), and lentigo senilis (40 +/- 7.2, n = 11) (mean age +/- SD). Each specimen was stained with Fontana-Masson for melanin pigments and immunohistochemical method for melanocytes. Quantitative analysis of melanin pigment and melanocyte number (density) were investigated through two methods: (1) two dermatologists measured the visual scales; and (2) computerized image analysis was used to measure melanin content indices (MCI). The data were evaluated using one-way ANOVA. The visual scale of the Fontana-Masson stain was the highest for lentigo senilis (3.8 +/- 0.40), followed by melasma (2.6 +/- 0.67), normal skin (1.8 +/- 0.60) and vitiligo (0) (P < 0.05). These findings were consistent with objective measurements made by computerized image analysis. MCI values were 120.3 +/- 20.74 for lentigo senilis, 81.1 +/- 19.27 for melasma, 45.5 +/- 16.92 for normal skin, and 0.3 +/- 0.30 for vitiligo in decreasing order (P < 0.05). MC/1E (melanocyte number per 1 mm epidermis) was about two fold larger in lentigo senilis (18.1 +/- 8.92) than melasma (9.7 +/- 2.40) or normal skin (9.3 +/- 2.67) (P < 0.05). MC/1B (melanocyte number per 1 mm basal layer) was about 1.5 fold higher in lentigo senilis (13.5 +/- 4.17), compared to normal skin (9.0 +/- 3.55) (P < 0.05). Melasma showed increased melanocyte numbers compared to normal skin, but it was not statistically significant (P > 0.05). We believe this computerized image analysis could be useful tool for diagnosis and comparison of interval changes in pigmentary diseases like melasma or lentigo senilis by quantifying melanin pigments or melanocytes in skin biopsy specimens.

(PDF emailed within 0-6 h: $19.90)

Accession: 045290061

Download citation: RISBibTeXText

PMID: 11277953

DOI: 10.1046/j.1365-4362.2001.00084.x

Related references

A preliminary study of skin electrical injury with computerized image analysis. Forensic Science International. 73(3): 202, 1995

Applications of computerized microscopic image analysis in infectious diseases. Reviews of Infectious Diseases 10(1): 92-102, 1988

Computerized image analysis in basic research and clinical application. Tandlakartidningen 79(8): 429-439, 1987

Improved identification of potentially dangerous pigmented skin lesions by computerized image analysis. Archives of Dermatology 139(2): 195-198, 2003

Ageing of the skin: study of elastic fiber network modifications by computerized image analysis. Gerontology 34(5-6): 291-296, 1988

Diagnosis of cutaneous melanoma: accuracy of a computerized image analysis system (Skin View). Skin Research and Technology 3(1): 23-27, 1997

Lichen growth and bioindication Application examples for computerized image analysis. International Journal of Mycology & Lichenology 5(1-2): 3-12, 1992

Application of computerized image analysis for description of fossil plant remains. Polish Botanical Studies, Guidebook series ( 23): 317-332, 1999

Computerized digital image analysis can improve the detection of potentially dangerous pigmented skin lesions. Journal of Investigative Dermatology 117(2): 512, August, 2001

Effect of testicular hyaluronidase on the macromolecules of the extracellular matrix of the skin a study by computerized image analysis. Pathologie Biologie 36(6): 833-838, 1988

Computerized image analysis in differentiation of skin lesions caused by electrocution, flame burns, and abrasion. Journal of Forensic Sciences 54(6): 1419-1422, 2010

Employment of image analysis methods in evaluation of extensivity of skin lesions in selected skin diseases. Journal of Investigative Dermatology 115(3): 591, September, 2000

Histochemical studies on monoamine oxidase and cholinesterase activity in the skin, especially in reference to pigmentary disturbance and allergic skin diseases. Japan J Dermatol Ser B 73(3): 260-268, 1963

Morphological characterization of the somatostatin-immunoreactive dendritic skin cells in urticaria pigmentosa patients by computerized image analysis. Scandinavian Journal of Immunology 21(5): 431-439, 1985

Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network. Melanoma Research 8(3): 261-266, 1998