2012
DOI: 10.12693/aphyspola.121.606
|View full text |Cite
|
Sign up to set email alerts
|

Epidermal Layers Characterisation by Opto-Magnetic Spectroscopy Based on Digital Image of Skin

Abstract: According to the most literature data, the skin is usually observed as a simple structure with equivalent electrical model, which includes general properties of epidermis, basal membrane and dermis. In this paper, we analyzed the skin structure as a more complex system. Particularly we analyzed epidermis based on layers approach and its water organization in lipids ordered in sub-layers. Using opto-magnetic spectroscopy method, which is very sensitive to paramagnetic/diamagnetic properties of the tissue, we fo… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
11
0

Year Published

2016
2016
2023
2023

Publication Types

Select...
7
1

Relationship

0
8

Authors

Journals

citations
Cited by 11 publications
(11 citation statements)
references
References 12 publications
0
11
0
Order By: Relevance
“…It indicates a minor difference in vessel skin's signal variation, and the standard deviation for the signals from the phantom skin and vessel were 0.41 and 0.15 (22% and 14.8%), respectively, that is in line with results from other groups (e.g., 13.8% [11]). The difficulty with this phantom was creating a uniform melanin layer (around 100-300 µm thick) with comparable thickness to the human pigmented layer [28]. However, because of the timeresolved selection of the PA signals from the vessels, the heterogeneity of this layer did not significantly influence the PA data from vessels and moving target's phantom.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…It indicates a minor difference in vessel skin's signal variation, and the standard deviation for the signals from the phantom skin and vessel were 0.41 and 0.15 (22% and 14.8%), respectively, that is in line with results from other groups (e.g., 13.8% [11]). The difficulty with this phantom was creating a uniform melanin layer (around 100-300 µm thick) with comparable thickness to the human pigmented layer [28]. However, because of the timeresolved selection of the PA signals from the vessels, the heterogeneity of this layer did not significantly influence the PA data from vessels and moving target's phantom.…”
Section: Discussionmentioning
confidence: 99%
“…Specifically, optical properties of human tissue at 808 nm (i.e., the laser wavelength used) are estimated to be as follows: reduced scattering coefficient μ s = 6.80-9.84 cm −1 and absorption coefficient μ a = 0.16-0.25 cm −1 [25]. A 200-µm-thick melanin layer was laid on top of the phantom surface which is similar to melanin layer thickness (100 µm to 300 µm) in humans [28]. The constituent materials for each desired layer were weighed by analytical balance and stirred.…”
Section: Blood Vessel Phantommentioning
confidence: 99%
“…For water loss prevention, proper structure and function of skin barrier are of the outmost importance: SC lipids (ceramides, cholesterol, and free fatty acids in almost equimolar ratio) form repeated lipid bilayers positioned among the corneocytes, parallel to the skin surface 5 . Thin water layers are located in the lipid phase, around the polar head groups 5‐7 . Water is able to stabilize the bilayer lipid structures, connecting the head groups with a strong network of hydrogen bonds 8 …”
Section: Introductionmentioning
confidence: 99%
“…It has a distinctive advantage of using digital RGB images which makes it more rapid and cost-effective compared to the hyperspectral imaging methods. Opto-magnetic imaging spectroscopy has been so far successfully applied in detection of viruses in human plasma [16] characterization of epidermal layers [17], skin oxygenation [18] and for discrimination of cancerous and healthy human tissues and cytological smears [19,20]. This paper presents results of application of this method in the characterization of meat and estimation of freshness using classification algorithms based on machine learning.…”
Section: Introductionmentioning
confidence: 99%