Age‐dependent skin texture analysis and evaluation using mobile camera image

2020

Sensors

Self Cite

With the development of the mobile phone, we can acquire high-resolution images of the skin to observe its detailed features using a mobile camera. We acquire stereo images using a mobile camera to enable a three-dimensional (3D) analysis of the skin surface. However, geometric changes in the observed skin structure caused by the lens distortion of the mobile phone result in a low accuracy of the 3D information extracted through stereo matching. Therefore, our study proposes a Distortion Correction Matrix (DCM) to correct the fine distortion of close-up mobile images, pixel by pixel. We verified the correction performance by analyzing the results of correspondence point matching in the stereo image corrected using the DCM. We also confirmed the correction results of the image taken at the five different working distances and derived a linear regression model for the relationship between the angle of the image and the distortion ratio. The proposed DCM considers the distortion degree, which appears to be different in the left and right regions of the image. Finally, we performed a fine distortion correction, which is difficult to check with the naked eye. The results of this study can enable the accurate and precise 3D analysis of the skin surface using corrected mobile images.

Section: Introductionmentioning

confidence: 56%

Section: Discussionmentioning

confidence: 99%

Adaptive Fine Distortion Correction Method for Stereo Images of Skin Acquired with a Mobile Phone

2020

Sensors

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“…The range of disease is expanding; it becomes difficult to distinguish the boundaries between the disease and the surrounding normal skin (4th column). These become factors that make accurate segmentation difficult 21 . All four algorithms generally perform accurate segmentation, even with challenging images.…”

Section: Resultsmentioning

confidence: 99%

Optimization of psoriasis assessment system based on patch images

Yoo

et al. 2021

Sci Rep

Psoriasis is a chronic inflammatory skin disease that occurs in various forms throughout the body and is associated with certain conditions such as heart disease, diabetes, and depression. The psoriasis area severity index (PASI) score, a tool used to evaluate the severity of psoriasis, is currently used in clinical trials and clinical research. The determination of severity is based on the subjective judgment of the clinician. Thus, the disease evaluation deviations are induced. Therefore, we propose optimal algorithms that can effectively segment the lesion area and classify the severity. In addition, a new dataset on psoriasis was built, including patch images of erythema and scaling. We performed psoriasis lesion segmentation and classified the disease severity. In addition, we evaluated the best-performing segmentation method and classifier and analyzed features that are highly related to the severity of psoriasis. In conclusion, we presented the optimal techniques for evaluating the severity of psoriasis. Our newly constructed dataset improved the generalization performance of psoriasis diagnosis and evaluation. It proposed an optimal system for specific evaluation indicators of the disease and a quantitative PASI scoring method. The proposed system can help to evaluate the severity of localized psoriasis more accurately.

“…However, a specialized equipment is challenging to use and expensive. Therefore, in our previous study, we acquired unconstrained skin images using a mobile device and conducted a skin microstructure analysis of the mobile images [8][9][10][11]. However, the analysis of the skin microstructure was difficult for topological changes such as blood vessels appearing prominently on the skin surface in the mobile images.…”

Section: Introductionmentioning

confidence: 99%

Skin Microstructure Segmentation and Aging Classification Using CNN-Based Models

2022

IEEE Access

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The skin surface is composed of a network-like microstructure comprising wrinkles. Observing and analyzing the microstructure of the skin that changes with the skin condition and aging are simple, stable, and accurate evaluation methods for skin diagnosis. However, the skin surface includes various morphological and topological changes, depending on the individual or the degree of aging. It is difficult to accurately extract and analyze a skin microstructure including these changes. Therefore, we perform skin microstructure segmentation and aging analysis by using convolutional neural network (CNN) models. First, we propose a fusion UNet model to extract the skin microstructure. We compare and evaluate the segmentation performance by using an image processing method and deep learning models. Next, we classify skin aging based on the skin microstructure. For the classification, we use four mobile CNN models: NASNet-Mobile, MobileNetV2, MobileNetV3-Small, and EfficientNet-B0. Subsequently, we compare and evaluate their classification performances. Results show that the segmentation images of the fusion U-Net are most similar to the ground truth, and the fusion U-Net model can detect fine wrinkles that are difficult to identify by the naked eye. In the microstructure-based classification of skin aging, MobileNetV3-Small exhibits the best performance with an accuracy of 94%. The proposed method facilitates an objective and quantitative analysis of the skin surface with more diverse aging characteristics. Consequently, the association between skin aging and skin microstructure changes is confirmed. Our study can be utilized in the diagnostic studies on various skin characteristics, including skin texture, anisotropy, and roughness. The proposed method can also be applied to a mobile-based self-diagnosis system.