Colour analysis of skin lesion regions for melanoma discrimination in clinical images

Transcriptomic and in vivo approaches introduced human iPSC-derived microvesicles for skin rejuvenation

The skin undergoes the formation of fine lines and wrinkles through the aging process; also, burns, trauma, and other similar circumstances give rise to various forms of skin ulcers. Induced pluripotent stem cells (iPSCs) have become promising candidates for skin healing and rejuvenation due to not stimulating inflammatory responses, low probability of immune rejection, high metabolic activity, good large-scale production capacity and potentials for personalized medicine. iPSCs can secrete microvesicles (MVs) containing RNA and proteins responsible for the normal repairing process of the skin. This study aimed to evaluate the possibility, safety and effectiveness of applying iPSCs-derived MVs for skin tissue engineering and rejuvenation applications. The possibility was assessed using the evaluation of the mRNA content of iPSC-derived MVs and the behavior of fibroblasts after MV treatment. Investigating the effect of microvesicle on stemness potential of mesenchymal stem cells was performed for safety concerns. In vivo evaluation of MVs was done in order to investigate related immune response, re-epithelialization and blood vessel formation to measure effectiveness. Shedding MVs were round in shape distributed in the range from 100 to 1000 nm in diameter and positive for AQP3, COL2A, FGF2, ITGB, and SEPTIN4 mRNAs. After treating dermal fibroblasts with iPSC-derived MVs, the expressions of collagens Iα1 and III transcripts (as the main fibrous extracellular matrix (ECM) proteins) were upregulated. Meanwhile, the survival and proliferation of MV treated fibroblasts did not change significantly. Evaluation of stemness markers in MV treated MSCs showed negligible alteration. In line with in vitro results, histomorphometry and histopathology findings also confirmed the helpful effect of MVs in skin regeneration in the rat burn wound models. Conducting more investigations on hiPSCs-derived MVs may lead to produce more efficient and safer biopharmaceutics for skin regeneration in the pharmaceutical market.

Performance Analysis of Low-Level and High-Level Intuitive Features for Melanoma Detection

This paper presents an intelligent approach for the detection of Melanoma—a deadly skin cancer. The first step in this direction includes the extraction of the textural features of the skin lesion along with the color features. The extracted features are used to train the Multilayer Feed-Forward Artificial Neural Networks. We evaluate the trained networks for the classification of test samples. This work entails three sets of experiments including 50 % , 70 % and 90 % of the data used for training, while the remaining 50 % , 30 % , and 10 % constitute the test sets. Haralick’s statistical parameters are computed for the extraction of textural features from the lesion. Such parameters are based on the Gray Level Co-occurrence Matrices (GLCM) with an offset of 2 , 4 , 8 , 12 , 16 , 20 , 24 and 28, each with an angle of 0 , 45 , 90 and 135 degrees, respectively. In order to distill color features, we have calculated the mean, median and standard deviation of the three color planes of the region of interest. These features are fed to an Artificial Neural Network (ANN) for the detection of skin cancer. The combination of Haralick’s parameters and color features have proven better than considering the features alone. Experimentation based on another set of features such as Asymmetry, Border irregularity, Color and Diameter (ABCD) features usually observed by dermatologists has also been demonstrated. The ‘D’ feature is however modified and named Oblongness. This feature captures the ratio between the length and the width. Furthermore, the use of modified standard deviation coupled with ABCD features improves the detection of Melanoma by an accuracy of 93.7 %

Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults

BACKGROUND

Early accurate detection of all skin cancer types is essential to guide appropriate management and to improve morbidity and survival. Melanoma and cutaneous squamous cell carcinoma (cSCC) are high-risk skin cancers which have the potential to metastasise and ultimately lead to death, whereas basal cell carcinoma (BCC) is usually localised with potential to infiltrate and damage surrounding tissue. Anxiety around missing early curable cases needs to be balanced against inappropriate referral and unnecessary excision of benign lesions. Computer-assisted diagnosis (CAD) systems use artificial intelligence to analyse lesion data and arrive at a diagnosis of skin cancer. When used in unreferred settings ('primary care'), CAD may assist general practitioners (GPs) or other clinicians to more appropriately triage high-risk lesions to secondary care. Used alongside clinical and dermoscopic suspicion of malignancy, CAD may reduce unnecessary excisions without missing melanoma cases.

OBJECTIVES

To determine the accuracy of CAD systems for diagnosing cutaneous invasive melanoma and atypical intraepidermal melanocytic variants, BCC or cSCC in adults, and to compare its accuracy with that of dermoscopy.

SEARCH METHODS

We undertook a comprehensive search of the following databases from inception up to August 2016: Cochrane Central Register of Controlled Trials (CENTRAL); MEDLINE; Embase; CINAHL; CPCI; Zetoc; Science Citation Index; US National Institutes of Health Ongoing Trials Register; NIHR Clinical Research Network Portfolio Database; and the World Health Organization International Clinical Trials Registry Platform. We studied reference lists and published systematic review articles.

SELECTION CRITERIA

Studies of any design that evaluated CAD alone, or in comparison with dermoscopy, in adults with lesions suspicious for melanoma or BCC or cSCC, and compared with a reference standard of either histological confirmation or clinical follow-up.

DATA COLLECTION AND ANALYSIS

Two review authors independently extracted all data using a standardised data extraction and quality assessment form (based on QUADAS-2). We contacted authors of included studies where information related to the target condition or diagnostic threshold were missing. We estimated summary sensitivities and specificities separately by type of CAD system, using the bivariate hierarchical model. We compared CAD with dermoscopy using (a) all available CAD data (indirect comparisons), and (b) studies providing paired data for both tests (direct comparisons). We tested the contribution of human decision-making to the accuracy of CAD diagnoses in a sensitivity analysis by removing studies that gave CAD results to clinicians to guide diagnostic decision-making.

MAIN RESULTS

We included 42 studies, 24 evaluating digital dermoscopy-based CAD systems (Derm-CAD) in 23 study cohorts with 9602 lesions (1220 melanomas, at least 83 BCCs, 9 cSCCs), providing 32 datasets for Derm-CAD and seven for dermoscopy. Eighteen studies evaluated spectroscopy-based CAD (Spectro-CAD) in 16 study cohorts with 6336 lesions (934 melanomas, 163 BCC, 49 cSCCs), providing 32 datasets for Spectro-CAD and six for dermoscopy. These consisted of 15 studies using multispectral imaging (MSI), two studies using electrical impedance spectroscopy (EIS) and one study using diffuse-reflectance spectroscopy. Studies were incompletely reported and at unclear to high risk of bias across all domains. Included studies inadequately address the review question, due to an abundance of low-quality studies, poor reporting, and recruitment of highly selected groups of participants.Across all CAD systems, we found considerable variation in the hardware and software technologies used, the types of classification algorithm employed, methods used to train the algorithms, and which lesion morphological features were extracted and analysed across all CAD systems, and even between studies evaluating CAD systems. Meta-analysis found CAD systems had high sensitivity for correct identification of cutaneous invasive melanoma and atypical intraepidermal melanocytic variants in highly selected populations, but with low and very variable specificity, particularly for Spectro-CAD systems. Pooled data from 22 studies estimated the sensitivity of Derm-CAD for the detection of melanoma as 90.1% (95% confidence interval (CI) 84.0% to 94.0%) and specificity as 74.3% (95% CI 63.6% to 82.7%). Pooled data from eight studies estimated the sensitivity of multispectral imaging CAD (MSI-CAD) as 92.9% (95% CI 83.7% to 97.1%) and specificity as 43.6% (95% CI 24.8% to 64.5%). When applied to a hypothetical population of 1000 lesions at the mean observed melanoma prevalence of 20%, Derm-CAD would miss 20 melanomas and would lead to 206 false-positive results for melanoma. MSI-CAD would miss 14 melanomas and would lead to 451 false diagnoses for melanoma. Preliminary findings suggest CAD systems are at least as sensitive as assessment of dermoscopic images for the diagnosis of invasive melanoma and atypical intraepidermal melanocytic variants. We are unable to make summary statements about the use of CAD in unreferred populations, or its accuracy in detecting keratinocyte cancers, or its use in any setting as a diagnostic aid, because of the paucity of studies.

AUTHORS' CONCLUSIONS

In highly selected patient populations all CAD types demonstrate high sensitivity, and could prove useful as a back-up for specialist diagnosis to assist in minimising the risk of missing melanomas. However, the evidence base is currently too poor to understand whether CAD system outputs translate to different clinical decision-making in practice. Insufficient data are available on the use of CAD in community settings, or for the detection of keratinocyte cancers. The evidence base for individual systems is too limited to draw conclusions on which might be preferred for practice. Prospective comparative studies are required that evaluate the use of already evaluated CAD systems as diagnostic aids, by comparison to face-to-face dermoscopy, and in participant populations that are representative of those in which the test would be used in practice.

The Role of Color and Morphologic Characteristics in Dermoscopic Diagnosis

IMPORTANCE

Both colors and structures are considered important in the dermoscopic evaluation of skin lesions but their relative significance is unknown.

OBJECTIVE

To determine if diagnostic accuracy for common skin lesions differs between gray-scale and color dermoscopic images.

DESIGN, SETTING, AND PARTICIPANTS

A convenience sample of 40 skin lesions (8 nevi, 8 seborrheic keratoses, 7 basal cell carcinomas, 7 melanomas, 4 hemangiomas, 4 dermatofibromas, 2 squamous cell carcinomas [SCCs]) was selected and shown to attendees of a dermoscopy course (2014 Memorial Sloan Kettering Cancer Center dermoscopy course). Twenty lesions were shown only once, either in gray-scale (n = 10) or color (n = 10) (nonpaired). Twenty lesions were shown twice, once in gray-scale (n = 20) and once in color (n = 20) (paired). Participants provided their diagnosis and confidence level for each of the 60 images. Of the 261 attendees, 158 participated (60.5%) in the study. Most were attending physicians (n = 76 [48.1%]). Most participants were practicing or training in dermatology (n = 144 [91.1%]). The median (interquartile range) experience evaluating skin lesions and using dermoscopy of participants was 6 (13.5) and 2 (4.0) years, respectively.

MAIN OUTCOMES AND MEASURES

Diagnostic accuracy and confidence level of participants evaluating gray-scale and color images. Two separate analyses were performed: (1) an unpaired evaluation comparing gray-scale and color images shown either once or for the first time, and (2) a paired evaluation comparing pairs of gray-scale and color images of the same lesion.

RESULTS

In univariate analysis of unpaired images, color images were less likely to be diagnosed correctly compared with gray-scale images (odds ratio [OR], 0.8; P < .001). Using gray-scale images as the reference, multivariate analyses of both unpaired and paired images found no association between correct lesion diagnosis and use of color images (OR, 1.0; P = .99, and OR, 1.2; P = .82, respectively). Stratified analysis of paired images using a color by diagnosis interaction term showed that participants were more likely to make a correct diagnosis of SCC and hemangioma in color (P < .001 for both comparisons) and dermatofibroma in gray-scale (P < .001).

CONCLUSIONS AND RELEVANCE

Morphologic characteristics (ie, structures and patterns), not color, provide the primary diagnostic clue in dermoscopy. Use of gray-scale images may improve teaching of dermoscopy to novices by emphasizing the evaluation of morphology.

A mathematical analysis of the ABCD criteria for diagnosing malignant melanoma

The medical community currently employs the ABCD (asymmetry, border irregularity, color variegation, and diameter of the lesion) criteria in the early diagnosis of a malignant melanoma. Although many image segmentation and classification methods are used to analyze the ABCD criteria, it is rare to see a study containing mathematical justification of the parameters that are used to quantify the ABCD criteria. In this paper, we suggest new parameters to assess asymmetry, border irregularity, and color variegation, and explain the mathematical meaning of the parameters. The suggested parameters are then tested with 24 skin samples. The parameters suggested for the 24 skin samples are displayed in three-dimensional coordinates and are compared to those presented in other studies (Ercal et al 1994 IEEE Trans. Biomed. Eng. 41 837-45, Cheerla and Frazier 2014 Int. J. Innovative Res. Sci., Eng. Technol. 3 9164-83) in terms of Pearson correlation coefficient and classification accuracy in determining the malignancy of the lesions.

Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis

Cutaneous melanoma is the most life-threatening form of skin cancer. Although advanced melanoma is often considered as incurable, if detected and excised early, the prognosis is promising. Today, clinicians use computer vision in an increasing number of applications to aid early detection of melanoma through dermatological image analysis (dermoscopy images, in particular). Colour assessment is essential for the clinical diagnosis of skin cancers. Due to this diagnostic importance, many studies have either focused on or employed colour features as a constituent part of their skin lesion analysis systems. These studies range from using low-level colour features, such as simple statistical measures of colours occurring in the lesion, to availing themselves of high-level semantic features such as the presence of blue-white veil, globules, or colour variegation in the lesion. This paper provides a retrospective survey and critical analysis of contributions in this research direction.

Hyperpigmentation after surgery for a deep dermal burn of the dorsum of the hand: partial-thickness debridement followed by medium split-thickness skin grafting vs full-thickness debridement followed by thick split-thickness skin grafting

BACKGROUND

Early excision and skin grafting are commonly used to treat deep dermal burns (DDBs) of the dorsum of the hand. Partial-thickness debridement (PTD) is one of the most commonly used procedures for the excision of burned tissue of the dorsum of the hand. In contrast, full-thickness debridement (FTD) has also been reported. However, it is unclear whether PTD or FTD is better.

METHODS

In this hospital-based retrospective study, we compared the outcomes of PTD followed by a medium split-thickness skin graft (STSG) with FTD followed by a thick STSG to treat a DDB of the dorsum of the hand in Japanese patients. To evaluate postoperative pigmentation of the skin graft, quantitative analyses were performed using the red, green, and blue (RGB) and the hue, saturation, and brightness (HSB) color spaces. We have organized the manuscript in a manner compliant with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement.

RESULTS

Data from 11 patients were analyzed. Six hands (five patients) received grafts in the PTD group and eight hands (six patients) received grafts in the FTD group. Graft take was significantly better in the FTD group (median 98 %, interquartile range 95-99) than in the PTD group (median 90 %, interquartile range 85-90) (P < 0.01). Quantitative skin color analyses in both the RGB and HSB color spaces showed that postoperative grafted skin was significantly darker than the adjacent control area in the PTD group, but not in the FTD group.

CONCLUSIONS

There is a possibility that FTD followed by a thick STSG is an option that can reduce the risk of hyperpigmentation after surgery for DDB of the dorsum of the hand in Japanese patients. Further investigation is needed to clarify whether the FTD or the thick STSG or both are the factor for the control of hyperpigmentation.

Modeling fade patterns of nipple areola complex tattoos following breast reconstruction

BACKGROUND

Nipple-areolar complex (NAC) tattoos are an effective cosmetic solution for creating a finished look following breast reconstruction procedures. NAC tattoos are prone to significant fading, leading patients to seek revisions. This study was designed to quantify changes in NAC tattoo appearance over time.

METHODS

A total of 71 images of 39 patients were analyzed for NAC tattoo color and shape by 5 blinded medical student graders using a customized scoring system. Subsequently, each image was analyzed using ColorPic software (Iconico, New York, NY). Red/green/blue and hue/saturation/value color parameters were collected. Color quantities were normalized to the individual patient's skin tone to control for variability in lighting. Spearman correlations and nonlinear regressions were calculated utilizing GraphPad Prism 6.0 (GraphPad, La Jolla, CA).

RESULTS

The length of time after tattoo placement inversely correlated with color score (P < 0.0001) and shape score (P = 0.0007). The time following tattoo placement was also inversely correlated with all quantitative color parameters. Each color parameter fit a 1-phase exponential decay model.

CONCLUSIONS

The decline in qualitative color and shape score agrees with clinical experience of tattoo quality declining over time. The color qualities of the tattoo approach those of the patient's skin over time, ultimately reaching a plateau. This can be modeled using a 1-phase decay equation. In practice, tattoo colors may be selected that compensate for the predictable changes that will occur. The results of this study will help optimize tattoo color and may alleviate the need for NAC tattoo revisions.

Proposed shade guide for human facial skin and lip: a pilot study

STATEMENT OF PROBLEM

Currently, no commercially available facial shade guide exists in the United States for the fabrication of facial prostheses.

PURPOSE

The purpose of this study was to measure facial skin and lip color in a human population sample stratified by age, gender, and race. Clustering analysis was used to determine optimal color coordinates for a proposed facial shade guide.

MATERIAL AND METHODS

Participants (n=119) were recruited from 4 racial/ethnic groups, 5 age groups, and both genders. Reflectance measurements of participants' noses and lower lips were made by using a spectroradiometer and xenon arc lamp with a 45/0 optical configuration. Repeated measures ANOVA (α=.05), to identify skin and lip color differences, resulting from race, age, gender, and location, and a hierarchical clustering analysis, to identify clusters of skin colors) were used.

RESULTS

Significant contributors to L*a*b* facial color were race and facial location (P<.01). b* affected all factors (P<.05). Age affected only b* (P<.001), while gender affected only L* (P<.05) and b* (P<.05). Analyses identified 5 clusters of skin color.

CONCLUSIONS

The study showed that skin color caused by age and gender primarily occurred within the yellow-blue axis. A significant lightness difference between gender groups was also found. Clustering analysis identified 5 distinct skin shade tabs.

Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided.

Decision support in dermatology and medicine: history and recent developments

This article is focused on diagnostic decision support tools and will provide a brief history of clinical decision support (CDS), examine the components of CDS and its associated terminology, and discuss recent developments in the use and application of CDS systems, particularly in the field of dermatology. For this article, we use CDS to mean an interactive system allowing input of patient-specific information and providing customized medical knowledge-based results via automated reasoning, for example, a set of rules and/or an underlying logic, and associations.

A basis function feature-based approach for skin lesion discrimination in dermatology dermoscopy images

BACKGROUND

Skin lesion color is an important feature for diagnosing malignant melanoma. New basis function correlation features are proposed for discriminating malignant melanoma lesions from benign lesions in dermoscopy images. The proposed features are computed based on correlating the luminance histogram of melanoma or benign labeled relative colors from a specified portion of the skin lesion with a set of basis functions. These features extend previously developed statistical and fuzzy logic-based relative color histogram analysis techniques for automated mapping of colors representative of melanoma and benign skin lesions from a training set of lesion images.

METHODS

Using the statistical and fuzzy logic-based approaches for relative color mapping, melanoma and benign color features are computed over skin lesion region of interest, respectively. Luminance histograms are obtained from the melanoma and benign mapped colors within the lesion region of interest and are correlated with a set of basis functions to quantify the distribution of colors. The histogram analysis techniques and feature calculations are evaluated using a data set of 279 malignant melanomas and 442 benign dysplastic nevi images.

RESULTS

Experimental test results showed that combining existing melanoma and benign color features with the proposed basis function features found from the melanoma mapped colors yielded average correct melanoma and benign lesion discrimination rates as high as 86.45% and 83.35%, respectively.

CONCLUSIONS

The basis function features provide an alternative approach to melanoma discrimination that quantifies the variation and distribution of colors characteristic of melanoma and benign skin lesions.

A relative color approach to color discrimination for malignant melanoma detection in dermoscopy images

BACKGROUND

Skin lesion color is an important feature for diagnosing malignant melanoma. In previous research, skin lesion color was investigated for discriminating malignant melanoma lesions from benign lesions in clinical images. Colors characteristics of melanoma were determined using color histogram analysis over a training set of images. Percent melanoma color and color clustering ratio features were used to quantify the presence of melanoma-colored pixels within skin lesions for skin lesion discrimination.

METHODS

In this research, the relative color histogram analysis technique is used to evaluate skin lesion discrimination based on color feature calculations in different regions of the skin lesion in dermoscopy images. The histogram analysis technique is examined for varying training set sizes from the set of 113 malignant melanomas and 113 benign dysplastic nevi images.

RESULTS

Experimental results show improved discrimination capability for feature calculations focused in the interior lesion region. Recognition rates for malignant melanoma and dysplastic nevi as high as 87.7% and 74.9%, respectively, are observed for the color clustering ratio computed using the outer 75% uniformly distributed area with a 10% offset within the boundary.

CONCLUSIONS

Experimental results appear to indicate that the melanoma color feature information is located in the interior of the lesion, excluding the 10% central-most region. The techniques presented here including the use of relative color and the determination of benign and malignant regions of the relative color histogram may be applicable to any set of images of benign and malignant lesions.

A systematic heuristic approach for feature selection for melanoma discrimination using clinical images

BACKGROUND

Numerous features are derived from the asymmetry, border irregularity, color variegation, and diameter of the skin lesion in dermatology for diagnosing malignant melanoma. Feature selection for the development of automated skin lesion discrimination systems is an important consideration.

METHODS

In this research, a systematic heuristic approach is investigated for feature selection and lesion classification. The approach integrates statistical-, correlation-, histogram-, and expert system-based components. Using statistical and correlation measures, interrelationships among features are determined. Expert system analysis is performed to identify redundant features. The feature selection process is applied to 19 shape and color features for a clinical image data set containing 355 malignant melanomas, 125 basal cell carcinomas, 177 dysplastic nevi, 199 nevocellular nevi, 139 seborrheic keratoses, and 45 vascular lesions.

RESULTS

Experimental results show reduced lesion classification error rates based on condensing the shape and color feature set from 19 features to 13 features using the feature selection process. Specifically, average test lesion classification error rates for discriminating malignant melanoma from non-melanoma lesions were reduced from 26.6% for 19 features to 23.2% for 13 features over five randomly generated training and test sets.

CONCLUSIONS

The experimental results show that the systematic heuristic approach for feature reduction can be successfully applied to achieve improved lesion discrimination. The feature reduction technique facilitates the elimination of redundant information that may inhibit lesion classification performance. The clinical application of this result is that automated skin lesion classification algorithm development can be fostered with systematic feature selection techniques.