The skin and other accessory structures of the integumentary system is the body’s most massive organ, and presents both advantages and challenges to image analysis and quantitative pathology. An advantage is the highly contrasting (and beautiful) stains that clearly identify various tissue types. The challenge is the constantly differential geometries in the skin layers within and across species.
A short look into the dermatology literature below gives some ideas and examples of what can be done with image analysis approaches. Most do not utilize whole section analysis:
ES Lee et al. 2001. Application of computerized image analysis in pigmentary skin diseases. Int J Derm 40(1):45-49. Used Fontana-Masson stain for melanin pigments and IHC for melanocytes, performed quantitative analysis of melanin pigment and melanocyte number (density).
A Viros et al, 2008. Improving Melanoma Classification by Integrating Genetic and Morphologic Features. PLoS Med 5(6):e120. Morphometric features measured to look at correlations with mutations, included increased upward migration and nest formation of intraepidermal melanocytes, thickening of involved epidermis, sharper demarcation of surrounding skin, size and roundness of tumor cells.
A Blum et al, 2004. 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 151(5):1029-38. Computer algorithm for the classification of melanocytic lesions as benign or malignant, with diagnostic accuracy of 82% for completely imaged lesions. Still requires a medical expert who is qualified to recognize cutaneous lesions as being of melanocytic origin.
K Aroni, 2010. Increased angiogenesis and mast cells in the centre compared to the periphery of vitiligo lesions. Arch Dermatol Res. 302(8):601-7. Specimens from lesional and perilesional nondepigmented skin were stained for mast cells, CD34 and VEGF. A significantly increased number of CD34 and VEGF positive vessels and mast cells were detected in the centre of the lesion than in the periphery. There was a positive correlation of CD34, VEGF and mast cell number between the centre and the periphery of the lesions.
P Paquet et al, 2005. Analytical quantification of the inflammatory cell infiltrate and CD95R expression during treatment of drug-induced toxic epidermal necrolysis. Arch Dermatol Res. 2005 Dec;297(6):266-73. In drug-induced toxic epidermal necrolysis (TEN), quantitative cell counts and densities were recorded for CD68 antigen (macrophages), CD45R0 antigen (activated T lymphocytes), Factor XIIIa (dermal dendrocytes) and the CD95 receptor (apoptosis marker).