Comparison between morphological parameters in pigmented skin lesion images acquired by means of epiluminescence surface microscopy and polarized-light videomicroscopy

Skin Cancer Classification With Deep Learning: A Systematic Review

Skin cancer is one of the most dangerous diseases in the world. Correctly classifying skin lesions at an early stage could aid clinical decision-making by providing an accurate disease diagnosis, potentially increasing the chances of cure before cancer spreads. However, achieving automatic skin cancer classification is difficult because the majority of skin disease images used for training are imbalanced and in short supply; meanwhile, the model’s cross-domain adaptability and robustness are also critical challenges. Recently, many deep learning-based methods have been widely used in skin cancer classification to solve the above issues and achieve satisfactory results. Nonetheless, reviews that include the abovementioned frontier problems in skin cancer classification are still scarce. Therefore, in this article, we provide a comprehensive overview of the latest deep learning-based algorithms for skin cancer classification. We begin with an overview of three types of dermatological images, followed by a list of publicly available datasets relating to skin cancers. After that, we review the successful applications of typical convolutional neural networks for skin cancer classification. As a highlight of this paper, we next summarize several frontier problems, including data imbalance, data limitation, domain adaptation, model robustness, and model efficiency, followed by corresponding solutions in the skin cancer classification task. Finally, by summarizing different deep learning-based methods to solve the frontier challenges in skin cancer classification, we can conclude that the general development direction of these approaches is structured, lightweight, and multimodal. Besides, for readers’ convenience, we have summarized our findings in figures and tables. Considering the growing popularity of deep learning, there are still many issues to overcome as well as chances to pursue in the future.

System for the Recognizing of Pigmented Skin Lesions with Fusion and Analysis of Heterogeneous Data Based on a Multimodal Neural Network

Simple Summary

Skin cancer is one of the most common cancers in humans. This study aims to create a system for recognizing pigmented skin lesions by analyzing heterogeneous data based on a multimodal neural network. Fusing patient statistics and multidimensional visual data allows for finding additional links between dermoscopic images and medical diagnostic results, significantly improving neural network classification accuracy. The use by specialists of the proposed system of neural network recognition of pigmented skin lesions will enhance the efficiency of diagnosis compared to visual diagnostic methods.

Abstract

Today, skin cancer is one of the most common malignant neoplasms in the human body. Diagnosis of pigmented lesions is challenging even for experienced dermatologists due to the wide range of morphological manifestations. Artificial intelligence technologies are capable of equaling and even surpassing the capabilities of a dermatologist in terms of efficiency. The main problem of implementing intellectual analysis systems is low accuracy. One of the possible ways to increase this indicator is using stages of preliminary processing of visual data and the use of heterogeneous data. The article proposes a multimodal neural network system for identifying pigmented skin lesions with a preliminary identification, and removing hair from dermatoscopic images. The novelty of the proposed system lies in the joint use of the stage of preliminary cleaning of hair structures and a multimodal neural network system for the analysis of heterogeneous data. The accuracy of pigmented skin lesions recognition in 10 diagnostically significant categories in the proposed system was 83.6%. The use of the proposed system by dermatologists as an auxiliary diagnostic method will minimize the impact of the human factor, assist in making medical decisions, and expand the possibilities of early detection of skin cancer.

Skin Lesion Segmentation with Improved Convolutional Neural Network

Recently, the incidence of skin cancer has increased considerably and is seriously threatening human health. Automatic detection of this disease, where early detection is critical to human life, is quite challenging. Factors such as undesirable residues (hair, ruler markers), indistinct boundaries, variable contrast, shape differences, and color differences in the skin lesion images make automatic analysis quite difficult. To overcome these challenges, a highly effective segmentation method based on a fully convolutional network (FCN) is presented in this paper. The proposed improved FCN (iFCN) architecture is used for the segmentation of full-resolution skin lesion images without any pre- or post-processing. It is to support the residual structure of the FCN architecture with spatial information. This situation, which creates a more advanced residual system, enables more precise detection of details on the edges of the lesion, and an analysis independent of skin color can be performed. It offers two contributions: determining the center of the lesion and clarifying the edge details despite the undesirable effects. Two publicly available datasets, the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Challenge and PH2 datasets, are used to evaluate the performance of the iFCN method. The mean Jaccard index is 78.34%, the mean Dice score is 88.64%, and the mean accuracy value is 95.30% for the proposed method for the ISBI 2017 test dataset. Furthermore, the mean Jaccard index is 87.1%, the mean Dice score is 93.02%, and the mean accuracy value is 96.92% for the proposed method for the PH2 test dataset.

Computational texture features of dermoscopic images and their link to the descriptive terminology: A survey

Computer-extracted texture features are relevant to diagnose cutaneous lesions such as melanomas. Our goal is to set a relationship between a well-established descriptive terminology, which describes the attributes of dermoscopic structures based on their aspect rather than their underlying causes, and the computational methods to extract texture-based features. By tackling this problem, we can ascertain what indicators used by dermatologists are reflected in the extracted texture features. We first review the state-of-the-art models for texture extraction in dermoscopic images. By comparing the methods' performance and goals, we conclude that (I) a single color space does not seem to give performances as good as using several ones, thus the latter is reasonable (II) the optimal number of extracted features seems to vary depending on the method's goal, and extracting a large number of features can lead to a loss of models robustness (III) methods such as GLCM, Sobel or Law energy filters are mainly used to capture local properties to detect specific dermoscopic structures (IV) methods that extract local and global features, like Gabor wavelets or SPT, tend to be used to analyze the presence of certain patterns of dermoscopic structures, e.g. globular, reticular, etc.

Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm

Skin lesion segmentation has a critical role in the early and accurate diagnosis of skin cancer by computerized systems. However, automatic segmentation of skin lesions in dermoscopic images is a challenging task owing to difficulties including artifacts (hairs, gel bubbles, ruler markers), indistinct boundaries, low contrast and varying sizes and shapes of the lesion images. This paper proposes a novel and effective pipeline for skin lesion segmentation in dermoscopic images combining a deep convolutional neural network named as You Only Look Once (YOLO) and the GrabCut algorithm. This method performs lesion segmentation using a dermoscopic image in four steps: 1. Removal of hairs on the lesion, 2. Detection of the lesion location, 3. Segmentation of the lesion area from the background, 4. Post-processing with morphological operators. The method was evaluated on two publicly well-known datasets, that is the PH2 and the ISBI 2017 (Skin Lesion Analysis Towards Melanoma Detection Challenge Dataset). The proposed pipeline model has achieved a 90% sensitivity rate on the ISBI 2017 dataset, outperforming other deep learning-based methods. The method also obtained close results according to the results obtained from other methods in the literature in terms of metrics of accuracy, specificity, Dice coefficient, and Jaccard index.

Visual inspection and dermoscopy, alone or in combination, for diagnosing keratinocyte skin cancers in adults

BACKGROUND

Early accurate detection of all skin cancer types is important to guide appropriate management, to reduce morbidity and to improve survival. Basal cell carcinoma (BCC) is almost always a localised skin cancer with potential to infiltrate and damage surrounding tissue, whereas a minority of cutaneous squamous cell carcinomas (cSCCs) and invasive melanomas are higher-risk skin cancers with the potential to metastasise and cause death. Dermoscopy has become an important tool to assist specialist clinicians in the diagnosis of melanoma, and is increasingly used in primary-care settings. Dermoscopy is a precision-built handheld illuminated magnifier that allows more detailed examination of the skin down to the level of the superficial dermis. Establishing the value of dermoscopy over and above visual inspection for the diagnosis of BCC or cSCC in primary- and secondary-care settings is critical to understanding its potential contribution to appropriate skin cancer triage, including referral of higher-risk cancers to secondary care, the identification of low-risk skin cancers that might be treated in primary care and to provide reassurance to those with benign skin lesions who can be safely discharged.

OBJECTIVES

To determine the diagnostic accuracy of visual inspection and dermoscopy, alone or in combination, for the detection of (a) BCC and (b) cSCC, in adults. We separated studies according to whether the diagnosis was recorded face-to-face (in person) or based on remote (image-based) assessment.

SEARCH METHODS

We undertook a comprehensive search of the following databases from inception up to August 2016: Cochrane Central Register of Controlled Trials; 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 visual inspection or dermoscopy or both in adults with lesions suspicious for skin cancer, 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 thresholds were missing. We estimated accuracy using hierarchical summary ROC methods. We undertook analysis of studies allowing direct comparison between tests. To facilitate interpretation of results, we computed values of sensitivity at the point on the SROC curve with 80% fixed specificity and values of specificity with 80% fixed sensitivity. We investigated the impact of in-person test interpretation; use of a purposely-developed algorithm to assist diagnosis; and observer expertise.

MAIN RESULTS

We included 24 publications reporting on 24 study cohorts, providing 27 visual inspection datasets (8805 lesions; 2579 malignancies) and 33 dermoscopy datasets (6855 lesions; 1444 malignancies). The risk of bias was mainly low for the index test (for dermoscopy evaluations) and reference standard domains, particularly for in-person evaluations, and high or unclear for participant selection, application of the index test for visual inspection and for participant flow and timing. We scored concerns about the applicability of study findings as of 'high' or 'unclear' concern for almost all studies across all domains assessed. Selective participant recruitment, lack of reproducibility of diagnostic thresholds and lack of detail on observer expertise were particularly problematic.The detection of BCC was reported in 28 datasets; 15 on an in-person basis and 13 image-based. Analysis of studies by prior testing of participants and according to observer expertise was not possible due to lack of data. Studies were primarily conducted in participants referred for specialist assessment of lesions with available histological classification. We found no clear differences in accuracy between dermoscopy studies undertaken in person and those which evaluated images. The lack of effect observed may be due to other sources of heterogeneity, including variations in the types of skin lesion studied, in dermatoscopes used, or in the use of algorithms and varying thresholds for deciding on a positive test result.Meta-analysis found in-person evaluations of dermoscopy (7 evaluations; 4683 lesions and 363 BCCs) to be more accurate than visual inspection alone for the detection of BCC (8 evaluations; 7017 lesions and 1586 BCCs), with a relative diagnostic odds ratio (RDOR) of 8.2 (95% confidence interval (CI) 3.5 to 19.3; P < 0.001). This corresponds to predicted differences in sensitivity of 14% (93% versus 79%) at a fixed specificity of 80% and predicted differences in specificity of 22% (99% versus 77%) at a fixed sensitivity of 80%. We observed very similar results for the image-based evaluations.When applied to a hypothetical population of 1000 lesions, of which 170 are BCC (based on median BCC prevalence across studies), an increased sensitivity of 14% from dermoscopy would lead to 24 fewer BCCs missed, assuming 166 false positive results from both tests. A 22% increase in specificity from dermoscopy with sensitivity fixed at 80% would result in 183 fewer unnecessary excisions, assuming 34 BCCs missed for both tests. There was not enough evidence to assess the use of algorithms or structured checklists for either visual inspection or dermoscopy.Insufficient data were available to draw conclusions on the accuracy of either test for the detection of cSCCs.

AUTHORS' CONCLUSIONS

Dermoscopy may be a valuable tool for the diagnosis of BCC as an adjunct to visual inspection of a suspicious skin lesion following a thorough history-taking including assessment of risk factors for keratinocyte cancer. The evidence primarily comes from secondary-care (referred) populations and populations with pigmented lesions or mixed lesion types. There is no clear evidence supporting the use of currently-available formal algorithms to assist dermoscopy diagnosis.

Dermoscopy, with and without visual inspection, for diagnosing melanoma in adults

BACKGROUND

Melanoma has one of the fastest rising incidence rates of any cancer. It accounts for a small percentage of skin cancer cases but is responsible for the majority of skin cancer deaths. Although history-taking and visual inspection of a suspicious lesion by a clinician are usually the first in a series of 'tests' to diagnose skin cancer, dermoscopy has become an important tool to assist diagnosis by specialist clinicians and is increasingly used in primary care settings. Dermoscopy is a magnification technique using visible light that allows more detailed examination of the skin compared to examination by the naked eye alone. Establishing the additive value of dermoscopy over and above visual inspection alone across a range of observers and settings is critical to understanding its contribution for the diagnosis of melanoma and to future understanding of the potential role of the growing number of other high-resolution image analysis techniques.

OBJECTIVES

To determine the diagnostic accuracy of dermoscopy alone, or when added to visual inspection of a skin lesion, for the detection of cutaneous invasive melanoma and atypical intraepidermal melanocytic variants in adults. We separated studies according to whether the diagnosis was recorded face-to-face (in-person), or based on remote (image-based), assessment.

SEARCH METHODS

We undertook a comprehensive search of the following databases from inception up to August 2016: 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 dermoscopy in adults with lesions suspicious for melanoma, compared with a reference standard of either histological confirmation or clinical follow-up. Data on the accuracy of visual inspection, to allow comparisons of tests, was included only if reported in the included studies of dermoscopy.

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 accuracy using hierarchical summary receiver operating characteristic (SROC),methods. Analysis of studies allowing direct comparison between tests was undertaken. To facilitate interpretation of results, we computed values of sensitivity at the point on the SROC curve with 80% fixed specificity and values of specificity with 80% fixed sensitivity. We investigated the impact of in-person test interpretation; use of a purposely developed algorithm to assist diagnosis; observer expertise; and dermoscopy training.

MAIN RESULTS

We included a total of 104 study publications reporting on 103 study cohorts with 42,788 lesions (including 5700 cases), providing 354 datasets for dermoscopy. The risk of bias was mainly low for the index test and reference standard domains and mainly high or unclear for participant selection and participant flow. Concerns regarding the applicability of study findings were largely scored as 'high' concern in three of four domains assessed. Selective participant recruitment, lack of reproducibility of diagnostic thresholds and lack of detail on observer expertise were particularly problematic.The accuracy of dermoscopy for the detection of invasive melanoma or atypical intraepidermal melanocytic variants was reported in 86 datasets; 26 for evaluations conducted in person (dermoscopy added to visual inspection), and 60 for image-based evaluations (diagnosis based on interpretation of dermoscopic images). Analyses of studies by prior testing revealed no obvious effect on accuracy; analyses were hampered by the lack of studies in primary care, lack of relevant information and the restricted inclusion of lesions selected for biopsy or excision. Accuracy was higher for in-person diagnosis compared to image-based evaluations (relative diagnostic odds ratio (RDOR) 4.6, 95% confidence interval (CI) 2.4 to 9.0; P < 0.001).We compared accuracy for (a), in-person evaluations of dermoscopy (26 evaluations; 23,169 lesions and 1664 melanomas),versus visual inspection alone (13 evaluations; 6740 lesions and 459 melanomas), and for (b), image-based evaluations of dermoscopy (60 evaluations; 13,475 lesions and 2851 melanomas),versus image-based visual inspection (11 evaluations; 1740 lesions and 305 melanomas). For both comparisons, meta-analysis found dermoscopy to be more accurate than visual inspection alone, with RDORs of (a), 4.7 (95% CI 3.0 to 7.5; P < 0.001), and (b), 5.6 (95% CI 3.7 to 8.5; P < 0.001). For a), the predicted difference in sensitivity at a fixed specificity of 80% was 16% (95% CI 8% to 23%; 92% for dermoscopy + visual inspection versus 76% for visual inspection), and predicted difference in specificity at a fixed sensitivity of 80% was 20% (95% CI 7% to 33%; 95% for dermoscopy + visual inspection versus 75% for visual inspection). For b) the predicted differences in sensitivity was 34% (95% CI 24% to 46%; 81% for dermoscopy versus 47% for visual inspection), at a fixed specificity of 80%, and predicted difference in specificity was 40% (95% CI 27% to 57%; 82% for dermoscopy versus 42% for visual inspection), at a fixed sensitivity of 80%.Using the median prevalence of disease in each set of studies ((a), 12% for in-person and (b), 24% for image-based), for a hypothetical population of 1000 lesions, an increase in sensitivity of (a), 16% (in-person), and (b), 34% (image-based), from using dermoscopy at a fixed specificity of 80% equates to a reduction in the number of melanomas missed of (a), 19 and (b), 81 with (a), 176 and (b), 152 false positive results. An increase in specificity of (a), 20% (in-person), and (b), 40% (image-based), at a fixed sensitivity of 80% equates to a reduction in the number of unnecessary excisions from using dermoscopy of (a), 176 and (b), 304 with (a), 24 and (b), 48 melanomas missed.The use of a named or published algorithm to assist dermoscopy interpretation (as opposed to no reported algorithm or reported use of pattern analysis), had no significant impact on accuracy either for in-person (RDOR 1.4, 95% CI 0.34 to 5.6; P = 0.17), or image-based (RDOR 1.4, 95% CI 0.60 to 3.3; P = 0.22), evaluations. This result was supported by subgroup analysis according to algorithm used. We observed higher accuracy for observers reported as having high experience and for those classed as 'expert consultants' in comparison to those considered to have less experience in dermoscopy, particularly for image-based evaluations. Evidence for the effect of dermoscopy training on test accuracy was very limited but suggested associated improvements in sensitivity.

AUTHORS' CONCLUSIONS

Despite the observed limitations in the evidence base, dermoscopy is a valuable tool to support the visual inspection of a suspicious skin lesion for the detection of melanoma and atypical intraepidermal melanocytic variants, particularly in referred populations and in the hands of experienced users. Data to support its use in primary care are limited, however, it may assist in triaging suspicious lesions for urgent referral when employed by suitably trained clinicians. Formal algorithms may be of most use for dermoscopy training purposes and for less expert observers, however reliable data comparing approaches using dermoscopy in person are lacking.

An Improved Skin Lesion Matching Scheme in Total Body Photography

Total body photography is used for early detection of malignant melanoma, primarily as a means of temporal skin surface monitoring. In a prior work, we presented a scanner with a set of algorithms to map and detect changes in pigmented skin lesions, thus demonstrating that it is possible to fully automate the process of total body image acquisition and processing. The key procedure in these algorithms is skin lesion matching that determines whether two images depict the same real lesion. In this paper, we aim to improve it with respect to false positive and negative outcomes. To this end, we developed two novel methods: one based on successive rigid transformations of three-dimensional point clouds and one based on nonrigid coordinate plane deformations in regions of interest around the lesions. In both approaches, we applied a robust outlier rejection procedure based on progressive graph matching. Using the images obtained from the scanner, we created a ground truth dataset tailored to diversify false positive match scenarios. The algorithms were evaluated according to their precision and recall values, and the results demonstrated the superiority of the second approach in all the tests. In the complete interpositional matching experiment, it reached a precision and recall as high as 99.92% and 81.65%, respectively, showing a significant improvement over our original method.

Standardization of terminology in dermoscopy/dermatoscopy: Results of the third consensus conference of the International Society of Dermoscopy

BACKGROUND

Evolving dermoscopic terminology motivated us to initiate a new consensus.

OBJECTIVE

We sought to establish a dictionary of standardized terms.

METHODS

We reviewed the medical literature, conducted a survey, and convened a discussion among experts.

RESULTS

Two competitive terminologies exist, a more metaphoric terminology that includes numerous terms and a descriptive terminology based on 5 basic terms. In a survey among members of the International Society of Dermoscopy (IDS) 23.5% (n = 201) participants preferentially use descriptive terminology, 20.1% (n = 172) use metaphoric terminology, and 484 (56.5%) use both. More participants who had been initially trained by metaphoric terminology prefer using descriptive terminology than vice versa (9.7% vs 2.6%, P < .001). Most new terms that were published since the last consensus conference in 2003 were unknown to the majority of the participants. There was uniform consensus that both terminologies are suitable, that metaphoric terms need definitions, that synonyms should be avoided, and that the creation of new metaphoric terms should be discouraged. The expert panel proposed a dictionary of standardized terms taking account of metaphoric and descriptive terms.

LIMITATIONS

A consensus seeks a workable compromise but does not guarantee its implementation.

CONCLUSION

The new consensus provides a revised framework of standardized terms to enhance the consistent use of dermoscopic terminology.

A new total body scanning system for automatic change detection in multiple pigmented skin lesions

The detection of newly appearing and changing pigmented skin lesions (PSLs) is essential for timely diagnosis of cutaneous melanoma. Total body skin examination (TBSE) procedures, currently practiced for this purpose, can be extremely time-consuming for patients with numerous lesions. In addition, these procedures are prone to subjectivity when selecting PSLs for baseline image comparison, increasing the risk of missing a developing cancer. To address this issue, we propose a new photogrammetry-based total body scanning system allowing for skin surface image acquisition using cross-polarized light. Equipped with 21 high-resolution cameras and a turntable, this scanner automatically acquires a set of overlapping images, covering 85%-90% of the patient's skin surface. These images are used for the automated mapping of PSLs and their change estimation between explorations. The maps produced relate images of individual lesions with their locations on the patient's body, solving the body-to-image and image-to-image correspondence problem in TBSEs. Currently, the scanner is limited to patients with sparse body hair and, for a complete skin examination, the scalp, palms, soles and inner arms should be photographed manually. The initial tests of the scanner showed that it can be successfully applied for automated mapping and temporal monitoring of multiple lesions: PSLs relevant for follow-up were repeatedly mapped in several explorations. Moreover, during the baseline image comparison, all lesions with artificially induced changes were correctly identified as "evolved."

Topical calcipotriol as a new therapeutic option for the treatment of clear cell acanthoma

Although uncommonly diagnosed, clear cell acanthoma represents an original source of speculative interest for dermatologists. Due to its clinical variability, it is often only recognized accidentally after histology. Dermoscopy has improved the reliability of clinical diagnosis of typical clear cell acanthoma thanks to the vascular pinpoint pattern and desquamative, peripheral collarette. Generally, therapy of clear cell acanthoma is oriented towards ablative solutions, such as surgery or cryotherapy. We propose a conservative therapy, based on the application of topical calcipotriol, which has produced complete regression after 2 months and no relapse one year after the end of treatment. A dermatoscope monitored all changes of clear cell acanthoma, showing its utility not only in diagnosis but also in therapeutic follow-up. This new therapeutic approach should support an inflammatory etiology of clear cell acanthoma, although further observations are needed to confirm this.

Impact of various color LED flashlights and different lighting source to skin distances on the manual and the computer-aided detection of basal cell carcinoma borders

BACKGROUND/AIMS

Quantitative analysis based on digital skin image has been proven to be helpful in dermatology. Moreover, the borders of the basal cell carcinoma (BCC) lesions have been challenging borders for the automatic detection methods. In this work, a computer-aided dermatoscopy system was proposed to enhance the clinical detection of BCC lesion borders.

METHODS

Fifty cases of BCC were selected and 2000 pictures were taken. The lesion images data were obtained with eight colors of flashlights and in five different lighting source to skin distances (SSDs). Then, the image-processing techniques were used for automatic detection of lesion borders. Further, the dermatologists marked the lesions on the obtained photos.

RESULTS

Considerable differences between the obtained values referring to the photographs that were taken at super blue and aqua green color lighting were observed for most of the BCC borders. It was observed that by changing the SSD, an optimum distance could be found where that the accuracy of the detection reaches to a maximum value.

CONCLUSION

This study clearly indicates that by changing SSD and lighting color, manual and automatic detection of BCC lesions borders can be enhanced.

Hair evaluation methods: merits and demerits

Various methods are available for evaluation (for diagnosis and/or quantification) of a patient presenting with hair loss. Hair evaluation methods are grouped into three main categories: Non-invasive methods (e.g., questionnaire, daily hair counts, standardized wash test, 60-s hair count, global photographs, dermoscopy, hair weight, contrasting felt examination, phototrichogram, TrichoScan and polarizing and surface electron microscopy), semi-invasive methods (e.g., trichogram and unit area trichogram) and invasive methods (e.g., scalp biopsy). Any single method is neither 'ideal' nor feasible. However, when interpreted with caution, these are valuable tools for patient diagnosis and monitoring. Daily hair counts, wash test, etc. are good methods for primary evaluation of the patient and to get an approximate assessment of the amount of shedding. Some methods like global photography form an important part of any hair clinic. Analytical methods like phototrichogram are usually possible only in the setting of a clinical trial. Many of these methods (like the scalp biopsy) require expertise for both processing and interpreting. We reviewed the available literature in detail in light of merits and demerits of each method. A plethora of newer methods is being introduced, which are relevant to the cosmetic industry/research. Such methods as well as metabolic/hormonal evaluation are not included in this review.

Raman microspectroscopy for skin cancer detection in vitro

We investigate the potential of near-infrared Raman microspectroscopy to differentiate between normal and malignant skin lesions. Thirty-nine skin tissue samples consisting of normal, basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma from 39 patients were investigated. Raman spectra were recorded at the surface and at 20-microm intervals below the surface for each sample, down to a depth of at least 100 microm. Data reduction algorithms based on the nonlinear maximum representation and discrimination feature (MRDF) and discriminant algorithms using sparse multinomial logistic regression (SMLR) were developed for classification of the Raman spectra relative to histopathology. The tissue Raman spectra were classified into pathological states with a maximal overall sensitivity and specificity for disease of 100%. These results indicate the potential of using Raman microspectroscopy for skin cancer detection and provide a clear rationale for future clinical studies.

Videodermoscopy in the evaluation of hair and scalp disorders

BACKGROUND

The standard methods used to diagnose scalp and hair disorders (eg, simple clinical inspection, pull test, biopsy) vary in sensitivity, reproducibility, and invasiveness. Studies on a few entities suggest that use of dermoscopy can improve clinical accuracy, but further investigation is needed.

OBJECTIVES

We sought to: (1) characterize features of several nontumoral scalp and hair conditions using videodermoscopy; and (2) assess the potential usefulness of videodermoscopy in the clinical evaluation of these conditions.

METHODS

Images (x20-70 magnification) obtained with videodermoscopy from 220 patients with various scalp and hair disorders and 15 unaffected control subjects were reviewed for distinguishing features.

RESULTS

Conditions evaluated included psoriasis (23), seborrheic dermatitis (26), alopecia areata (58), androgenetic alopecia (64), chronic telogen effluvium (7), trichotillomania (12), and primary cicatricial alopecia (30). Clinical features evident to the naked eye were seen in great detail when videodermoscopy was used. Novel features (eg, yellow dots in alopecia areata) were also identified.

LIMITATIONS

Findings require confirmation by blinded, prospective investigation.

CONCLUSIONS

Use of videodermoscopy in the clinical evaluation of scalp and hair disorders improves diagnostic capability beyond simple clinical inspection and reveals novel features of disease, which may extend clinical and pathogenetic understanding.

Instrument-, age- and site-dependent variations of dermoscopic patterns of congenital melanocytic naevi: a multicentre study

BACKGROUND

Recently, we identified and described dermoscopic aspects, present with a higher frequency in congenital melanocytic lesions with respect to acquired naevi. We also classified small- and medium-sized congenital naevi (CN) into nine subtypes according to their macroscopic and dermoscopic aspects.

OBJECTIVES

Because the recognition of dermoscopic features may be instrument dependent, in this study, we wanted to check whether dermoscopic patterns specific for CN can be identified in digital images acquired by means of different instruments. We also wanted to check the validity of our previously proposed classification and assess possible age- and site-dependent variations of dermoscopic patterns and naevus subtypes.

PATIENTS/METHODS

Images corresponding to 384 small- or medium-sized CN were collected in eight different centres employing four different instruments. Lesion images were evaluated and checked for the presence of specific dermoscopic criteria, classified, and compared with a database of 350 acquired naevi.

RESULTS

Specific and unspecific dermoscopic features were identifiable in images acquired by means of all four instrument types. The mean number of identified features per lesion did not vary according to the instrument employed for the acquisition of the images; however, it was lower for lesions recorded employing low magnifications. The previously proposed classification was easily applied to the whole image database. The variegated naevus type was identified as a highly specific clinical/dermoscopic pattern. Dermoscopic features varied according to age and location. The globular type prevailed in subjects under 11 years of age and on the trunk, whereas the majority of reticular lesions were located on the limbs.

CONCLUSIONS

Because definite clinical and histological criteria for the diagnosis of the congenital nature of naevi are lacking, the use of dermoscopy can be of great help in identifying those lesions where the presence of specific dermoscopic features makes the diagnosis of CN more likely. Moreover, dermoscopy can be useful both for the classification of lesions already identified as congenital according to definite clinical and anamnestic data and for a possible correlation of naevus phenotype and dermoscopic patterns to the risk of developing a malignant melanoma in prospective studies.

Practical color calibration for dermoscopy, applied to a digital epiluminescence microscope

BACKGROUND/PURPOSE

The assessment of colors is essential for melanoma (MM) diagnosis, both for pattern analysis on dermoscopic images, and when using semiquantitative methods. Our aim was to provide a simple, precise characterization and reproducible calibration of the color response for dermoscopic instruments.

METHODS

Three processes were used to correct the non-uniform illumination pattern of the instrument, to easily estimate the camera gamma settings and to describe the color space conversion matrices required to produce standard images, in any color space. A specific color space was also developed to optimize the representation of dermatoscopic colors. The calibration technique was tested both on synthetic reference surfaces and on real images by comparing the difference between the images colors obtained with two different equipments.

RESULTS

The differences between the images acquired by means of the two instruments, calculated on the reference patterns after calibration, were up to 10 times lower then before, while comparison of histograms referring to real images provided an improvement of about seven times on average.

CONCLUSIONS

A complete workflow for dermatologic image calibration, which allows the user to continue using his own software and algorithms, but with a much higher informative content, is presented. The technique is simple and may improve cooperation between different research centers, in teleconsulting contexts or for result comparisons.

Acquired melanocytic lesions and the decision to excise: role of color variegation and distribution as assessed by dermoscopy

BACKGROUND

Because melanoma may sometimes be difficult to differentiate from nevi with clinical atypia, many benign lesions also undergo surgical removal.

OBJECTIVE

To assess color type and distribution in dermoscopic melanocytic lesion images and to analyze the influence of color parameters on the diagnostic process and the decision to excise.

METHODS

Overall, 603 images, referring to 112 melanomas and 491 nevi, were retrospectively subdivided into four groups: "clearly benign," "follow-up," "dermoscopic atypical nevi," and "dermoscopic melanomas," according to their dermoscopic aspects. The frequency of color type, number, and asymmetry were evaluated on digital images.

RESULTS

With respect to lesions not eligible for excision according to dermoscopy (but excised for cosmetic reasons), those excised with a suspicion of malignancy showed a higher number of colors, whose distribution was also more asymmetric. Moreover, the frequency of the presence of black and blue-gray progressively increased from clearly benign lesions to atypical nevi and dermoscopic melanomas.

CONCLUSION

In dermoscopic images, color parameters are essential elements for the diagnosis of atypical nevus, which can be differentiated from both a clearly benign lesion and a melanoma. Furthermore, pigmentation asymmetry and the presence of blue-gray represent the main color features, which should lead to the decision to excise.

Is JPEG compression of videomicroscopic images compatible with telediagnosis? Comparison between diagnostic performance and pattern recognition on uncompressed TIFF images and JPEG compressed ones

Early melanoma diagnosis is an important goal for dermatologists. Polarized light systems are increasingly employed for dermatoscopic diagnosis of melanocytic lesions. For the purpose of teledermoscopy, whose importance is increasingly growing for consultation and teaching purposes, it is of utmost importance to establish whether, after compression, polarized light images maintain their informativeness. The aim of our study was to check the effects of compression on melanocytic lesion images acquired by means of a digital videomicroscope on the identification of morphological details of the image and on diagnostic accuracy. A total of 170 50-fold-magnified pigmented skin lesion images, acquired in Tagged Image File Format (TIFF) by a digital videomicroscope, were compressed using Joint Photographic Experts Group (JPEG) algorithms (compression factor 30). Two experts in videomicroscopy evaluated both original and compressed images twice by describing single lesion features and expressing a diagnosis. Reproducibility in the assessment of dermoscopic parameters and observer performance were studied by kappa statistics and Receiver Operating Characteristic (ROC) analysis. Both intra- and interobserver reproducibility in the assessment of morphological details were higher when TIFF images were considered, indicating a better image quality. Nonetheless, there was no significant difference in the diagnostic accuracy between uncompressed images and compressed ones, although the intraobserver reproducibility in the diagnostic judgement was higher for uncompressed images. Despite loss in image details, factor 30 compressed videomicroscopic images enable a good diagnostic accuracy.

Skin imaging: is it clinically useful?

Non-invasive skin imaging techniques have proliferated over the last decade. Whilst most have a research role, some are routinely used in dermatology clinics. Of these, the skin surface microscope (dermatoscope), a diagnostic aid for pigmented lesions, has had most clinical impact. Such devices, when linked to a videomicroscope for computer analysis, have been dubbed as 'mole scanners'. Mole scanners are increasingly available on a commercial basis even though computer diagnosis of pigmented lesions is currently no better than diagnosis by human experts. Meanwhile, other imaging techniques, such as high-resolution ultrasonography, spectroscopy and optical coherence tomography, may yet find a role in diagnosis and disease monitoring.

Automated description of colours in polarized-light surface microscopy images of melanocytic lesions

The aim of this study was to develop a computerized method for the identification and description of colour areas in melanocytic lesion images based on an approach mimicking the human perception of colours. A colour palette comprising six colour groups (black, dark brown, light brown, blue-grey, red and white) was created by selecting single colour components within melanocytic lesion images acquired using a digital videomicroscope, and was implemented in the image analysis program. For each colour region, the area, the distance from the lesion centroid, the spread, the colour area distribution in the internal and the external part of the lesion, and asymmetries were assessed on 604 melanocytic lesion images in our image database. Black, white and blue-grey colour areas were detected more frequently in melanomas compared with naevi. Moreover, significant differences in colour descriptors were observed for each colour group, showing that colour areas are more unevenly distributed in melanomas compared with naevi. Using a discriminant analysis approach, the extension of dark, white and blue-grey areas and some descriptors of the distribution of the colour areas were identified as the most relevant colour parameters for differentiating between benign and malignant lesions. In conclusion, our automatic procedure breaks down the image into the colour areas used in the clinical examination process, and also supplies a description of their extension and distribution, with parameters that correlate with the clinical concepts of regularity and homogeneity.

Instruments and new technologies for the in vivo diagnosis of melanoma

The principal objective of screening individuals at risk for melanoma is detection of cutaneous melanoma during the curable stages of its early evolution. Unaided visual inspection of the skin is often suboptimal at diagnosing melanoma. Improving the diagnostic accuracy for melanoma remains an area of active research. These research efforts have focused on both the detection of early melanoma and the in-depth evaluation of suspicious pigmented lesions for the presence or absence of melanoma. Numerous instruments are under investigation to determine their usefulness in imaging and ascertaining a correct in vivo diagnosis of melanoma. It is anticipated that some of these tools, alone or in combination, will improve our ability to differentiate, in vivo, melanoma from its simulators. Ultimately, these advances may prevent unnecessary biopsies (increased specificity) while increasing the sensitivity for diagnosing melanoma. This article reviews the current instruments and new technologies for the in vivo diagnosis of melanoma. Learning objective At the conclusion of this learning activity, participants should be acquainted with the instruments designed to facilitate the early detection of melanoma. They should also be familiar with the basic technology behind these instruments and should recognize the potential benefits and limitations inherent in each.

Computer description of colours in dermoscopic melanocytic lesion images reproducing clinical assessment

BACKGROUND

The assessment of colours is essential for the diagnosis of malignant melanoma (MM), both for pattern analysis on dermoscopic images, and when employing semiquantitative methods.

OBJECTIVES

To develop a computer program for colour assessment in MM images mimicking the human perception of lesion colours, and to compare the automatic colour evaluation with one performed by human observers.

METHODS

A colour palette comprising six colour groups (black, dark brown, light brown, blue-grey, red and white) was created by selecting single colour components inside melanocytic lesion images acquired by means of a digital videomicroscope, and was implemented in the image analysis program. Subsequently, colours were assessed by the computer program on 331 melanocytic lesion images composing our image database, and the results were compared with the evaluation of lesion colours performed by the clinician.

RESULTS

The black, white and blue-grey colours were more frequently found in MMs than in naevi, both by the clinicians and by the computer. In MM images we observed 4.27 +/- 1.14 colours (mean + or - SD) per lesion, as opposed to 3.22 +/- 0.68 in naevi. The correlation between clinical and computer evaluation of the colours was very good, with a value of 0.781 for overall assessment.

CONCLUSIONS

This innovative method for automatic colour evaluation, reproducing clinical assessment of melanocytic lesion colours, may provide numerical parameters to be employed for computer-aided diagnosis of MM.