Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

Polanía, Luisa F.; Bala, Raja; Purwar, Ankur; Matts, Paul J.; Maltz, Martin

doi:10.2352/issn.2470-1173.2020.14.coimg-357

Cited by 2 publications

(2 citation statements)

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“…In spite of evident limitations of the used model, the proposed 3D representation approach demonstrated not only selectivity concerning the type of pathology but also some advantages if compared to the previously used approach – planar mapping of the chromophore distributions over the skin malformation. 1 , 2 , 5 , 17 – 20 The planar chromophore maps are well-suited for assessment of a specific single pathology sample, but this representation lacks sense of general trends of chromophore distributions in the pathology groups. Qualitatively, the main criteria for the first stage study are fulfilled: all hemangiomas have exhibited higher oxyhemoglobin concentration increase than other examined pathologies and all pigmented malformations (nevi, basaliomas, and melanoma) have exhibited higher melanin concentration increase.…”

Section: Discussionmentioning

confidence: 99%

Three-dimensional representation of triple spectral line imaging data as an option for noncontact skin diagnostics

et al. 2022

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. Significance: Skin malformations in dermatology are mostly evaluated subjectively, based on a doctor’s experience and visual perception; an option for objective quantitative skin assessment is camera-based spectrally selective diagnostics. Multispectral imaging is a technique capable to provide information about concentrations of the absorbing chromophores and their distribution over the malformation in a noncontact way. Conversion of spectral images into distribution maps of chromophores can be performed by means of the modified Beer–Lambert law. However, such distribution maps represent only single specific cases, therefore, some extensive method for data comparison is needed. Aim: This study aims to develop a more informative approach for identification and characterization of skin malformations using three-dimensional (3D) representation of triple spectral line imaging data. Approach: The 3D-representation method is experimentally tested on eight different skin pathology types, including both benign and malignant pathologies; an imaging device ensuring uniform three laser line (448, 532, and 659 nm) illumination is used. Three spectral line images are extracted from a single snapshot RGB image data, with subsequent calculation of attenuation coefficients for each working wavelength at every image pixel and represented as 3D graphs. Skin chromophore content variations in malformations are represented in a similar way. Results: Clinical measurement results for 99 skin pathologies, including basal cell carcinomas, melanoma, dermal nevi, combined nevi, junctional nevi, blue nevi, seborrheic keratosis, and hemangiomas. They are presented as 3D spectral attenuation maps exhibiting specific individual features for each group of pathologies. Along with intensity attenuation maps, 3D maps for content variations of three main skin chromophores (melanin, oxyhemoglobin, and deoxyhemoglobin), calculated in frame of a model based on modified Beer–Lambert law, are also presented. Advantages and disadvantages of the proposed data representation method are discussed. Conclusions: The described 3D-representation method of triple spectral line imaging data shows promising potential for objective quantitative noncontact diagnosis of skin pathologies.

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Section: Discussionmentioning

confidence: 99%

Three-dimensional representation of triple spectral line imaging data as an option for noncontact skin diagnostics

et al. 2022

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“…Recently, different applications of artificial intelligence (AI), such as computer vision and machine learning algorithms, have been utilized in the cosmetics industry for the automated detection and analysis of image-based data, mostly as a face skin diagnostic tool [17,18,19]. In those assessments, the AI was validated by parallel assessments conducted by expert dermatologists, who normally use validated scales for skin ageing assessment.…”

Section: Hair Assembly Properties and Artificial Intelligencementioning

confidence: 99%

Artificial Intelligence in hair research: A proof‐of‐concept study on evaluating hair assembly features

Daniels¹,

Tamburić²,

Benini

et al. 2021

Intern J of Cosmetic Sci

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Objective The first objective of this study was to apply computer vision and machine learning techniques to quantify the effects of haircare treatments on hair assembly and to identify correctly whether unknown tresses were treated or not. The second objective was to explore and compare the performance of human assessment with that obtained from artificial intelligence (AI) algorithms. Methods Machine learning was applied to a data set of hair tress images (virgin and bleached), both untreated and treated with a shampoo and conditioner set, aimed at increasing hair volume whilst improving alignment and reducing the flyway of the hair. The automatic quantification of the following hair image features was conducted: local and global hair volumes and hair alignment. These features were assessed at three time points: t0 (no treatment), t1 (two treatments) and t2 (three treatments). Classifier tests were applied to test the accuracy of the machine learning. A sensory test (paired comparison of t0 vs t2) and an online front image‐based survey (paired comparison of t0 vs t1, t1 vs t2, t0 vs t2) were conducted to compare human assessment with that of the algorithms. Results The automatic image analysis identified changes to hair volume and alignment which enabled the successful application of the classification tests, especially when the hair images were grouped into untreated and treated groups. The human assessment of hair presented in pairs confirmed the automatic image analysis. The image assessment for both virgin hair and bleached only partially agreed with the analysis of the subset of images used in the online survey. One hypothesis is that treatments changed somewhat the shape of the hair tress, with the effect being more pronounced in bleached hair. This made human assessment of flat images more challenging than when viewed directly in 3D. Overall, the bleached hair exhibited effects of higher magnitude than the virgin hair. Conclusions This study illustrated the capacity of artificial intelligence for hair image detection and classification, and for image analysis of hair assembly features following treatments. The human assessment partially confirmed the image analysis and highlighted the challenges imposed by the presentation mode.

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Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

Abstract: Fast track article for IS&T International Symposium on Electronic Imaging 2020: Computational Imaging proceedings.

Cited by 2 publications

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Three-dimensional representation of triple spectral line imaging data as an option for noncontact skin diagnostics

Three-dimensional representation of triple spectral line imaging data as an option for noncontact skin diagnostics

Artificial Intelligence in hair research: A proof‐of‐concept study on evaluating hair assembly features

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