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. 2019 Apr-Jun;9(2):88-99.
doi: 10.4103/jmss.JMSS_52_18.

Objective Assessment of Skin Repigmentation Using a Multilayer Perceptron

Juan Fernando Chica  1 , Sayonara Zaputt  2 , Javier Encalada  2 , Christian Salamea  1 , Melissa Montalvo  1
Affiliations

Objective Assessment of Skin Repigmentation Using a Multilayer Perceptron

Juan Fernando Chica et al. J Med Signals Sens. 2019 Apr-Jun.

Abstract

Background: Vitiligo is a pathology that causes the appearance of achromic macules on the skin that can spread on to other areas of the body. It is estimated that it affects 1.2% of the world population and can disrupt the mental state of people in whom this disease has developed, generating negative feelings that can become suicidal in the worst of cases. The present work focuses on the development of a support tool that allows to objectively quantifying the repigmentation of the skin.

Methods: We propose a novel method based on artificial neural networks that use characteristics of the interaction of light with the skin to determine areas of healthy skin and skin with vitiligo. We used photographs of specific areas of skin containing vitiligo. We select as independent variables: the type of skin, the amount of skin with vitiligo and the amount of repigmented skin. Considering these variables, the experiments were organized in an orthogonal table. We analyzed the result of the method based on three parameters (sensitivity, specificity, and F1-Score) and finally, its results were compared with other methods proposed in similar research.

Results: The proposed method demonstrated the best performance of the three methods, and it also showed its capability to detect healthy skin and skin with vitiligo in areas up to 1 × 1 pixels.

Conclusion: The results show that the proposed method has the potential to be used in clinical applications. It should be noted that the performance could be significantly improved by increasing the training patterns.

Keywords: Artificial neural network; multilayer perceptron; objective assessment; skin repigmentation; vitiligo.

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Conflict of interest statement

There are no conflicts of interest.

Figures

Figure 1
Figure 1
Absorption spectrum of the skinīs predominant chromophores: (a) response profile of Eumelanin, it can be noted that although there is no maximum absorption in the region of visible light, it increases progressively when approaching the electromagnetic spectrum shortwave; Image based on.[28] Absorption spectrum of the skinīs predominant chromophores (b) response profile of oxy-hemoglobin (solid line) and deoxyhemoglobin (dashed line). Image based on[28]
Figure 2
Figure 2
Operation diagram of an artificial neural network
Figure 3
Figure 3
Structure of a multilayer perceptron with one hidden layer
Figure 4
Figure 4
Level of reflection of the skin in red-green-blue images: (a) Original skin image. Level of reflection of the skin in red-green-blue images: (b) Information of the intensities of the red-green-blue image obtained from row 126 represented by a horizontal black line in the original image
Figure 5
Figure 5
Level of reflection of the skin in red-green-blue images: (a) Original skin image. Level of reflection of the skin in red-green-blue images: (b) Information of the intensities of red-green-blue image obtained from row 202 represented by a horizontal black line in the original image
Figure 6
Figure 6
Featured extraction of images, (a) Input of multilayer perceptron. Featured extraction of images, (b) Desired Output
Figure 7
Figure 7
Proposed architecture of multilayer perceptron. It should be noticed that the proposed method analyze the image pixel to pixel
Figure 8
Figure 8
Pattern images constructed based on Table 1: (a-h) Test 1–7. It can be noticed that is a modeled skin
Figure 9
Figure 9
Pattern images constructed based on Table 2: (a) Test 9, (b) test 10, (c) test 11, (d) test 12, (e) test 13, (f) test 14, (g) test 15, (h) test 16, (i) test 17, (j) test 18, (k) test 17, (l) test 17
Figure 10
Figure 10
Responses of the three methods to the test 1: (a) Pattern image, (b) desired response, (c) independent component analysis method, (d) Fuzzy C-Means method, (e) proposed method
Figure 11
Figure 11
Responses of the three methods to the test 8: (a) Pattern image, (b) desired response, (c) independent component analysis method, (d) Fuzzy C-Means method, (e) proposed method
Figure 12
Figure 12
Responses of the three methods to the test 9: (a) Pattern image, (b) desired response, (c) independent component analysis method, (d) Fuzzy C-Means method, (e) proposed method
Figure 13
Figure 13
Responses of the three methods to the test 20: (a) pattern image, (b) independent component analysis method, (c) Fuzzy C-Means method, (d) proposed method
See this image and copyright information in PMC

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