Validation of Fespixon in Postoperative Scar Cosmesis Using Quantitative Digital Photography Analysis

Huang, Tien-Ru; Chen, Shyi-Gen; Chen, Jui-Ching; Liu, Shao‐Cheng

doi:10.1093/asj/sjad005

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…The Euclidean distance between two color vectors in CIELAB color space (Equation ( 2)) is known as the DeltaE color difference. DeltaE has been successfully employed in several studies to quantify skin color differences using digital photography [21][22][23]. In the present study, we additionally explored DeltaE94, a modified formula that is proposed to represent the human perception of color differences better than DeltaE [24].…”

Section: Perceptual Color Similaritymentioning

confidence: 99%

Image Perceptual Similarity Metrics for the Assessment of Basal Cell Carcinoma

Spyridonos

Gaitanis

Likas

et al. 2023

Cancers

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Efficient management of basal cell carcinomas (BCC) requires reliable assessments of both tumors and post-treatment scars. We aimed to estimate image similarity metrics that account for BCC’s perceptual color and texture deviation from perilesional skin. In total, 176 clinical photographs of BCC were assessed by six physicians using a visual deviation scale. Internal consistency and inter-rater agreement were estimated using Cronbach’s α, weighted Gwet’s AC2, and quadratic Cohen’s kappa. The mean visual scores were used to validate a range of similarity metrics employing different color spaces, distances, and image embeddings from a pre-trained VGG16 neural network. The calculated similarities were transformed into discrete values using ordinal logistic regression models. The Bray–Curtis distance in the YIQ color model and rectified embeddings from the ‘fc6’ layer minimized the mean squared error and demonstrated strong performance in representing perceptual similarities. Box plot analysis and the Wilcoxon rank-sum test were used to visualize and compare the levels of agreement, conducted on a random validation round between the two groups: ‘Human–System’ and ‘Human–Human.’ The proposed metrics were comparable in terms of internal consistency and agreement with human raters. The findings suggest that the proposed metrics offer a robust and cost-effective approach to monitoring BCC treatment outcomes in clinical settings.

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Section: Perceptual Color Similaritymentioning

confidence: 99%

Image Perceptual Similarity Metrics for the Assessment of Basal Cell Carcinoma

Spyridonos

Gaitanis

Likas

et al. 2023

Cancers

View full text Add to dashboard Cite

show abstract

Ιmage Perceptual Similarity Metrics for the Assessment of Basal Cell Carcinoma

Spyridonos¹,

Likas²,

Seretis³

et al. 2023

Preprint

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Efficient management of basal cell carcinoma (BCC) requires reliable assessments of both tumors and post-treatment scars. We aimed to estimate image similarity metrics that account for BCC's perceptual color and texture deviation from perilesional skin. 176 clinical photographs of BCC were assessed by six physicians using a visual deviation scale. Internal consistency and inter-rater agreement were estimated using Cronbach's α, weighted Gwet's AC2, and quadratic Cohen's Kappa. The mean visual scores were used to validate a range of similarity metrics employing different color spaces, distances, and image embeddings from pre-trained VGG16 neural network. The calculated similarities were transformed into discrete values using ordinal logistic regression models. The Bray-Curtis distance in the YIQ color model and rectified embeddings from the 'fc6' layer minimized the mean squared error and demonstrated strong performance in representing perceptual similarities. Box plot analysis and Wilcoxon rank-sum test were used to visualize and compare the levels of agreement, conducted on a random validation round between the two groups: ‘Human-System’ and ‘Human-Human’. The proposed metrics were comparable in terms of internal consistency and agreement with human raters. The findings suggest that the proposed metrics offer a robust, and cost-effective approach to monitoring BCC treatment outcomes in clinical settings.

show abstract