A rudimentary system for automatic discrimination among basic skin lesions on the basis of color analysis of video images

Takiwaki, Hirotsugu; Shirai, Shiro; Watanabe, Yukina; Nakagawa, Kohji; Arase, Seiji

doi:10.1016/0190-9622(95)90344-5

Cited by 17 publications

(16 citation statements)

References 9 publications

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“…Further investigation has to be carried out to evaluate the performance of the system with respect of this kind of MLs. Anyway, with the exception of few studies in which a very high number of cases were used (19), this number is within the range reported in the other studies (11)(12)(13)(14)(15)(16)(17)(18).…”

Section: Discussionsupporting

confidence: 79%

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Clinical validation of an automated system for supporting the early diagnosis of melanoma

Sboner¹,

Bauer

Zumiani

et al. 2004

Skin Research and Technology

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Section: Discussionsupporting

confidence: 79%

“…To avoid any bias in clinical judgment, computer-aided diagnosis systems have been introduced. Several automatic systems were proposed for the early diagnosis of melanoma, using different approaches (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22).…”

Section: And Blackwell Munksgaard 2004 Accepted For Publication 2 Janumentioning

confidence: 99%

Clinical validation of an automated system for supporting the early diagnosis of melanoma

Sboner¹,

Bauer

Zumiani

et al. 2004

Skin Research and Technology

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“…Among the different parameters characterizing a scar, the color of the skin is an important component that is largely determined by the distribution of blood vessels and pigmentation. Computerized image analysis of skin colors using a pixel averaging procedure had been proposed as a simple cost‐effective and noninvasive method to evaluate scar color …”

mentioning

confidence: 99%

Digital imaging analysis to assess scar phenotype

Smith

Nidey

Miller

et al. 2014

Wound Repair Regeneration

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In order to understand the link between the genetic background of patients and wound clinical outcomes, it is critical to have a reliable method to assess the phenotypic characteristics of healed wounds. In this study, we present a novel imaging method that provides reproducible, sensitive and unbiased assessments of post-surgical scarring. We used this approach to investigate the possibility that genetic variants in orofacial clefting genes are associated with suboptimal healing. Red-green-blue (RGB) digital images of post-surgical scars of 68 patients, following unilateral cleft lip repair, were captured using the 3dMD image system. Morphometric and colorimetric data of repaired regions of the philtrum and upper lip were acquired using ImageJ software and the unaffected contralateral regions were used as patient-specific controls. Repeatability of the method was high with interclass correlation coefficient score > 0.8. This method detected a very significant difference in all three colors, and for all patients, between the scarred and the contralateral unaffected philtrum (P ranging from 1.20−05 to 1.95−14). Physicians’ clinical outcome ratings from the same images showed high inter-observer variability (overall Pearson coefficient = 0.49) as well as low correlation with digital image analysis results. Finally, we identified genetic variants in TGFB3 and ARHGAP29 associated with suboptimal healing outcome.

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“…22,23 These methods are based on computerized analysis of digital images obtained by ELM. [24][25][26][27][28][29][30][31][32][33][34] In the present study we used a digital dermoscopy analyzer (DDA) with a series of "clinically difficult" PSL, both malignant and benign in nature. We evaluated a large set of morphologic variables.…”

Section: For Editorial Comment See Page 1538mentioning

confidence: 99%

Digital Dermoscopy Analysis for the Differentiation of Atypical Nevi and Early Melanoma

Andreassi¹,

Perotti²,

Rubegni³

et al. 1999

Arch Dermatol

125

102

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Objectives: To use a digital dermoscopy analyzer with a series of "borderline" pigmentary skin lesions (ie, clinically atypical nevi and early melanoma) to find correlation between the studied variables and to determine their discriminating power with respect to histological diagnosis.Design: A total of 147 pigmentary skin lesions were histologically examined by 3 experienced dermatopathologists and identified as nevi (n = 90) and melanomas (n = 57). The system evaluated 36 variables to be studied as possible discriminant variables, grouped into 4 categories: geometries, colors, textures, and islands of color.Setting: University medical department.Patients: A sample of patients with excised pigmen-tary skin lesions (nevi and melanomas).Main Outcome Measures: Sensitivity, specificity, and accuracy of the model for evaluating "borderline" pigmentary skin lesions.Results: After multivariate stepwise discriminant analysis, only 13 variables were selected to compute the canonical discriminant function. Conclusion:The present method made it possible to determine which objective variables are important for distinguishing atypical benign pigmentary skin lesions and early melanoma.

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A rudimentary system for automatic discrimination among basic skin lesions on the basis of color analysis of video images

Cited by 17 publications

References 9 publications

Clinical validation of an automated system for supporting the early diagnosis of melanoma

Clinical validation of an automated system for supporting the early diagnosis of melanoma

Digital imaging analysis to assess scar phenotype

Digital Dermoscopy Analysis for the Differentiation of Atypical Nevi and Early Melanoma

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