Enhancement of lesion classification using divergence, curl and curvature of skin pattern

BACKGROUND/PURPOSE: The observation that skin pattern tends to be disrupted by malignant but not by benign skin lesions suggests that measurements of skin pattern disruption on simply captured white light optical clinical (WLC) skin images could be a useful contribution to a diagnostic feature set. Previous work which generated a flow field of skin pattern using a measurement of local line direction and variation determined by the minimum eigenvalue and its corresponding eigenvector of the local tensor matrix to measure skin pattern disruption was computationally low cost and encouraging. This paper explores the possibility of extracting new features from the first and second differentiations of this flow field to enhance classification performance. METHODS: The skin pattern was extracted from WLC skin images by high-pass filtering. The skin line direction was estimated using a local image gradient matrix to produce a flow field of skin pattern. Divergence, curl, mean and Gaussian curvatures of this flow field were computed from the first and second differentiations of this flow field. The difference of these measures combined with skin line direction across the lesion image boundary was used as a lesion classifier. RESULTS: A set of images of malignant melanoma and benign naevi were analysed as above and the scatter plot in a two-dimensional dominant feature space using principal component analysis showed excellent separation of benign and malignant lesions. A receiver operating characteristic plot enclosed an area of 0.96. CONCLUSIONS: The experimental results show that the divergence, curl, mean and Gaussian curvatures of the flow field can increase lesion classifier accuracy. Combined with skin line direction they are promising features for distinguishing malignant melanoma from benign lesions and the methods used are computationally efficient which is important if their use is to be considered in clinical practice.     

Combination of features from skin pattern and ABCD analysis for lesion classification

BACKGROUND/PURPOSE: It is known that the standard features for lesion classification are ABCD features, that is, asymmetry, border irregularity, colour variegation and diameter of lesion. However, the observation that skin patterning tends to be disrupted by malignant but not by benign skin lesions suggests that measurements of skin pattern disruption on simply captured white light optical skin images could be a useful contribution to a diagnostic feature set. Previous work using both skin line direction and intensity for lesion classification was encouraging. But these features have not been combined with the ABCD features. This paper explores the possibility of combing features from skin pattern and ABCD analysis to enhance classification performance. METHODS: The skin line direction and intensity were extracted from a local tensor matrix of skin pattern. Meanwhile, ABCD analysis was conducted to generate six features. They were asymmetry, border irregularity, colour (red, green and blue) variegations and diameter of lesion. The eight features of each case were combined using a principal component analysis (PCA) to produce two dominant features for lesion classification. RESULTS: A larger set of images containing malignant melanoma (MM) and benign naevi were processed as above and the scatter plot in a two-dimensional dominant feature space showed excellent separation of benign and malignant lesions. An ROC (receiver operating characteristic) plot enclosed an area of 0.94. CONCLUSIONS: The classification results showed that the individual features have a limited discrimination capability and the combined features were promising to distinguish MM from benign lesion.     

Analysis of skin line pattern for lesion classification

Background/purpose: It has been observed that skin patterning tends to be disrupted by malignant but not by benign skin lesions. This suggests that measurements of skin pattern disruption on simply captured white light optical skin images could be a useful contribution to a diagnostic feature set. Previous work using a measurement of line strength by a consistent high-value profiling technique followed by local variance measurement or a region agglomerative classifier to measure skin line pattern disruption was extremely promising but computationally intensive, suggesting that the idea of measuring skin pattern disruption was useful but a simpler method was required.
Methods: The skin pattern was extracted by high-pass filtration and enhanced by adaptive anisotropic (spatial variant) filtering which smoothes along skin lines but not across them. The skin line main direction and direction variance were estimated using a local image gradient matrix and the difference of these measures across the lesion image boundary was used as a lesion classifier.
Results: A set of images of malignant melanoma and benign naevi were processed as above and the scatter plot of results in a two-dimensional feature (line direction and line variation difference) space showed excellent separation of benign and malignant lesions. An ROC plot enclosed an area of 0.88.
Conclusions: The experimental results showed that the local line direction and the local line variation were promising features for distinguishing malignant melanoma from benign lesion and the methods used were effective and computationally low-cost.     

Lesion classification using skin patterning

Background/aims: The observation that skin line patterning tends to be disrupted by malignant but not non‐malignant skin lesions suggests that this could be used as an aid to lesion differentiation. Since recognised differentiating features can be obtained from the simply‐captured white light optical image, the possibility of using such images for skin pattern disruption detection is worth exploring. Methods: The skin pattern has been extracted from optical images by high‐pass filtering and profiles of local line strength variation with the angle estimated using a new consistent high‐value profiling technique. The resultant profile images have been analysed using a novel region‐based agglomerative clustering technique (mRAC) and also a local variance measurement. A measure based on the relationship between the classification results and an intensity‐based segmentation was calculated, and this represented the disruption of the skin line patterning. Results: A set of images containing a variety of histologically confirmed malignant and non‐malignant lesions was analysed. The computed textural disruption figure was compared to both the histological diagnosis and to a visual estimate of patterning disruption for each image. It was demonstrated that lesion separation could be achieved by both analysis methods, with a good correlation with visual estimate of disruption and with mRAC providing the best performance. Conclusions: It was concluded that the acquisition and modelling of skin line patterning from clinical images of skin lesions had been successfully achieved and that the analysis of the resulting data provided an assessment of pattern disruption that is both consistent with visual inspection and effective in presenting information useful for discrimination between melanoma and benign naevi lesion examples.     

Using 3D differential forms to characterize a pigmented lesion in vivo

Background/purpose: After the formulation of ABCD rules, many new feature extraction methods are emerging to describe the asymmetry, border irregularity, color variation and diameter of malignant melanoma. In this paper, a new research direction orthogonal to ABCD rules that characterizes 3D local disruption of skin surfaces to realize automatic recognition of melanoma is described.
Methods: This paper examines 3D differential forms of skin surfaces to characterize the local geometrical properties of melanoma. Firstly, 3D data of skin surfaces are obtained using a photometric stereo device. Then differential forms of lesion surfaces are determined to describe the geometrical texture patterns involved. Using only these geometrical features, a simple least-squared error-based linear classifier can be constructed to realize the classification of malignant melanomas and benign lesions.
Results: As with the 3D data of 35 melanoma and 66 benign lesion samples collected from local pigmented lesion clinics, the optimal sensitivity and specificity of the constructed linear classifier are 71.4% and 86.4%, respectively. The total area enclosed by the corresponding receiver operating characteristics curve is 0.823.
Conclusion: This study indicates that differential forms obtained from 3D data are very promising in characterizing melanoma. Combining these features with other skin features such as border irregularity and color variation might further improve the accuracy and reliability of the automatic diagnosis of melanoma.     

Obtaining malignant melanoma indicators through statistical analysis of 3D skin surface disruptions

Background/purpose: It has been observed that disruptions in skin patterns are larger for malignant melanoma (MM) than benign lesions. In order to extend the classification results achieved for 2D skin patterns, this work intends to investigate the feasibility of lesion classification using 3D skin surface texture, in the form of surface normals acquired from a previously built six-light photometric stereo device.
Material and methods: The proposed approach seeks to separate MM from benign lesions through analysis of the degree of surface disruptions in the tilt and slant direction of surface normals, so called skin tilt pattern and skin slant pattern. A 2D Gaussian function is used to simulate a normal region of skin for comparison with a lesion's observed tilt and slant patterns. The differences associated with the two patterns are estimated as the disruptions in the tilt and slant pattern respectively for lesion classification.
Results: Preliminary studies on 11 MMs and 28 benign lesions have given Receiver operating characteristic areas of 0.73 and 0.85 for tilt and slant pattern, respectively, which are better than 0.65 previously obtained for the skin line direction using the same samples.
Conclusions: This paper has demonstrated an important application of 3D skin texture for computer-assisted diagnosis of MM in vivo . By taking advantage of the extra dimensional information, preliminary studies suggest that some improvements over the existing 2D skin line pattern approach for the differentiation between MM and benign lesions.     

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.     

Simulation and analysis of optical skin lesion images

BACKGROUND/PURPOSE: In order to properly analyse the effectiveness of methods for optically differentiating malignant from benign skin lesions, it is necessary to have a set of images for which the ground truth is known. However, aspects of the ground truth of clinical images such as true lesion boundary position are unknown or not known precisely. Therefore, a skin/lesion image simulation with known features including boundary location, skin pattern and lesion colour is needed to enable accurate assessment of feature estimation algorithms for lesion classification. METHODS: In this paper, monochrome and colour skin/lesion images are synthesised with known characteristics including boundary, colour and skin pattern. Skin pattern is simulated with segmented lines with variations in length, orientation and intensity. Skin and lesion textures are modelled by an auto-regressive (AR) process. Monochrome skin lesion images are obtained by combining monochrome skin and lesion textures under the control of a known lesion shape with the addition of skin pattern. Colour skin lesion images are generated by mixing coloured skin and lesion textures. Finally, an inflammation area and image artefacts such as hair and specular reflection are added. RESULTS: The synthesised images provide the image set for evaluating image pre-processing, segmentation and skin pattern analysis. The pre-processing includes hair removal and specular reflection reduction. An AR model interpolation is suggested for hair removal, and multiple illumination processing is developed to decrease specular reflection. A fast snake algorithm is extended to detect the boundaries of skin lesion and inflammation areas. Skin line direction is detected as a feature to measure the disruption of skin pattern caused by lesion. CONCLUSIONS: Simulation of monochrome and colour skin/lesion image has been investigated, which is an alternative way to provide image set with known characteristics to validate image processing algorithms for image pre-processing, lesion/inflammation boundary detection and skin pattern analysis.     

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.     

Skin lesion classification using relative color features

BACKGROUND/PURPOSE: Clinically, it is difficult to differentiate the early stage of malignant melanoma and certain benign skin lesions due to similarity in appearance. Our research uses image analysis of clinical skin images and relative color-based pattern recognition techniques to enhance the images and improve differentiation of these lesions. METHODS: First, the relative color images were created from digitized photographic images. Then, they were segmented into objects and morphologically filtered. Next, the relative color features were extracted from the objects to form two different feature spaces; a lesion feature space and an object feature space. The two feature spaces serve as two data models to be analyzed to determine the best features. Finally, we used a statistical analysis model of relative color features, which better classifies the various types of skin lesions. RESULTS/CONCLUSIONS: The best features found for differentiation of melanoma and benign skin lesions from this study are area, mean in the red and blue bands, standard deviation in the red and green bands, skewness in the green band, and entropy in the red band. With the relative color feature algorithm developed, the best results were obtained with a multi-layer perceptron neural network model. This showed an overall classification success of 79%, with 70% of the benign lesions successfully classified, and 86% of malignant melanoma successfully classified.     

Dermoscopy of pigmented lesions on mucocutaneous junction and mucous membrane

Background The dermoscopic features of pigmented lesions on the mucocutaneous junction and mucous membrane are different from those on hairy skin. Differentiation between benign lesions and malignant melanomas of these sites is often difficult. Objective To define the dermoscopic patterns of lesions on the mucocutaneous junction and mucous membrane, and assess the applicability of standard dermoscopic algorithms to these lesions. Patients and methods An unselected consecutive series of 40 lesions on the mucocutaneous junction and mucous membrane was studied. All the lesions were imaged using dermoscopy devices, analysed for dermoscopic patterns and scored with algorithms including the ABCD rule, Menzies method, 7‐point checklist, 3‐point checklist and the CASH algorithm. Results Benign pigmented lesions of the mucocutaneous junction and mucous membrane frequently presented a dotted‐globular pattern (25%), a homogeneous pattern (25%), a fish scale‐like pattern (18·8%) and a hyphal pattern (18·8%), while melanomas of these sites showed a multicomponent pattern (75%) and a homogeneous pattern (25%). The fish scale‐like pattern and hyphal pattern were considered to be variants of the ring‐like pattern. The sensitivities of the ABCD rule, Menzies method, 7‐point checklist, 3‐point checklist and CASH algorithm in diagnosing mucosal melanomas were 100%, 100%, 63%, 88% and 100%; and the specificities were 100%, 94%, 100%, 94% and 100%, respectively. Conclusion The ring‐like pattern and its variants (fish scale‐like pattern and hyphal pattern) are frequently observed as well as the dotted‐globular pattern and homogeneous pattern in mucosal melanotic macules. The algorithms for pigmented lesions on hairy skin also apply to those on the mucocutaneous junction and mucous membrane with high sensitivity and specificity.     

Laypersons' perceptual discrimination of pigmented skin lesions

BACKGROUND: Most cutaneous malignant melanomas of the skin are visible and should, at least in theory, be possible to detect with the naked eye. OBJECTIVE: This study was conducted to learn more about laypersons' ability to discriminate between benign pigmented lesions and malignant ones. METHODS: Four groups of laypersons (n = 120) were asked to evaluate pictures of different types of pigmented skin lesions, before and after they received information about the ABCD (asymmetry, border irregularity, color variegation, and diameter greater than 6 mm) criteria, with respect to the necessity of action. RESULTS: The respondents made adequate assessments of melanomas but overestimated the danger of benign pigmented skin lesions. Information about the ABCD criteria enhanced their ability to make adequate assessments. CONCLUSION: People seem to make adequate decisions concerning how to act if they have a melanoma. On the other hand, common moles and dysplastic nevi were harder to discriminate. Providing information to the public about the features of melanomas, in accordance with the ABCD criteria, might help laypersons in their perceptual discrimination of skin lesions.     

Assessment of pigmented skin lesions in terms of blood perfusion estimates

Background/aims: Cutaneous malignant melanoma is a disease of increasing clinical and economical importance. The prognosis is good with early diagnosis. The chief differential diagnosis is benign melanocytic naevus, a common lesion in Caucasians. Attempts have been made to use bioengineering techniques to aid in the initial diagnosis. The present study proposes a method of extracting possibly discriminative blood perfusion properties in pigmented skin lesions by combining information on the lesions' blood perfusion with optical or visual information of their spatial extent. Methods: A total of 46 blood perfusion measurements were performed on 22 pigmented skin lesions, the ultimate diagnosis of which was three histologically proven malignant melanomas, four histologically proven benign naevi and fifteen naevi assessed by two specialist dermatologists as being benign. Laser Doppler perfusion imaging gave two different types of two‐dimensional data sets (64×64 pixels), one representing the total backscattered light intensity at each measurement point (TLI image) and the second corresponding to perfusion values. The boundaries of each examined lesion were derived from the TLI image employing greyscale thresholding, thus resulting in an estimated region of interest (ROI) approximating the optical extent of the lesion. The ROI was superimposed on the perfusion image and extraction of perfusion features was then performed. Results: The processing of the TLI images was successful in delineating the lesions' boundaries. The first hypothesis that the mean perfusion quotients in MM and benign naevi are equal could not be rejected at the chosen 5% level of significance. The second hypothesis that the mean percent‐age of elevated perfusion values (image pixels) within the ROI shows no difference between MM and benign naevi could be rejected at a 5% level of significance. Conclusions: This study has presented a method of extracting blood perfusion parameters of pigmented skin lesions by combining blood perfusion information with information on the lesion's optical extent. The proposed method of presenting data could prove to be a useful discriminative adjunct in the assessment of pigmented skin lesions.     

Benign dermoscopic network patterns in dysplastic melanocytic nevi

Background/aims: Epiluminescence microscopy (ELM) is a non-invasive clinical technique, which by employing the optical phenomenon of oil immersion makes surface structures of the skin accessible for in vivo examination and provides additional criteria for the diagnosis of pigment skin lesions (PSLs). Many ELM criteria have been described. One of the most important ELM criteria is the pigment network (PN).
Objective: The aim of this study is to identify benign ELM (dermoscopic) network patterns of dysplastic melanocytic nevi (DMN).
Methods: This study included 907 dysplastic melanocytic nevi in 178 patients. Prior to biopsy, each lesion was photographed with oil immersion, and the images were viewed on a high-resolution compact slide projector. For each PSL, the ELM Network Features and ABCD-score were evaluated.
Results and discussion: The benign dermoscopic network features in DMN are the presents of a regular PN with delicate lines and margins, which predominantly thins out at the border of the lesion. For DMN, with these features, the mean ABCD score is smaller than ABCD-score for DMNs with irregular, prominent PN and network patches, ending abruptly at the periphery. In DMN with a network predominantly thinning out at the border of the lesion several uniform network patterns were found—diffuse network pattern, patchy network pattern, structureless center pattern, globular center pattern, and pigmented-blotch center pattern.
Conclusions: Benign features of pigment network are regularity, delicacy and thinning out at the border of the lesion. Benign dermoscopic network patterns are diffuse network pattern, patchy network pattern, structureless center pattern, globular center pattern, and pigmented-blotch center pattern. They can be found in DMN with a network predominantly thinning out at the border of the lesion.     

Morphologic changes of pigmented skin lesions: a useful extension of the ABCD rule for dermatoscopy

BACKGROUND: Epiluminescence microscopy (ELM) significantly increases the early diagnosis of pigmented skin lesions (PSL) using established criteria and pattern analysis. The ABCD rule for dermatoscopy (ie, ELM) provides a simplified approach to the interpretation of ELM images on the basis of asymmetry (A), border (B), color (C), and dermatoscopic structure (D). OBJECTIVE: We set out to determine whether the diagnostic accuracy of the ABCD scoring algorithm can be significantly improved by incorporating information about morphologic changes of the lesion observed and provided by the patient. METHODS: We prospectively collected 356 small pigmented skin lesions (< 1 cm) including 73 (20.5%) melanomas. Before excision all patients were asked whether the lesion had changed in size, color, or shape within the last year or whether they experienced any sign of ulceration or spontaneous bleeding. ELM images of the lesions were evaluated according to the ABCD rule for dermatoscopy to yield a semiquantitative score. Accuracy of diagnosis was evaluated in terms of sensitivity, specificity, and area under receiver operating characteristic curves (AUC). RESULTS: The frequency of reported changes was significantly higher for melanomas than benign PSL (65.8% vs 29.7%, P < .001). In a multivariate model morphologic change was a significant independent predictor of malignancy (odds ratio = 3.17, 95% confidence interval [CI]: 1.96 to 5.14, P < .001). The mean final score achieved when using the enhanced ABCD-E criteria including morphologic change (E) was significantly higher for melanomas (5.7, 95% CI: 5.3 to 6.0) than benign PSL (2.9, 95% CI: 2.8 to 3.1, P < .001). Diagnostic accuracy was significantly higher when the lesions were evaluated by the enhanced ABCD-E criteria as compared with the standard ABCD score (AUC(ABCD) = 0.87 vs AUC(ABCD-E) = 0.90; P = .006). CONCLUSION: Information about morphologic changes of PSL as reported by the patient is a useful extension of the ABCD rule for dermatoscopy.     

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

Background: Skin lesion colour is an important feature for diagnosing malignant melanoma. Colour histogram analysis over a training set of images has been used to identify colours characteristic of melanoma, i.e., melanoma colours. A percent melanoma colour feature defined as the percentage of the lesion pixels that are melanoma colours has been used as a feature to discriminate melanomas from benign lesions.
Methods: In this research, the colour histogram analysis technique is extended to evaluate skin lesion discrimination based on colour feature calculations in different regions of the skin lesion. The colour features examined include percent melanoma colour and a novel colour clustering ratio. Experiments are performed using clinical images of 129 malignant melanomas and 129 benign lesions consisting of 40 seborrheic keratoses and 89 nevocellular nevi.
Results: Experimental results show improved discrimination capability for feature calculations focused in the lesion boundary region. Specifically, correct melanoma and benign recognition rates are observed as high as 89 and 83%, respectively, for the percent melanoma colour feature computed using only the outermost, uniformly distributed 10% of the lesion's area.
Conclusions: The experimental results show for the features investigated that the region closest to the skin lesion boundary contains the greatest colour discrimination information for lesion screening. Furthermore, the percent melanoma colour feature consistently outperformed the colour clustering ratio for the different skin lesion regions examined. The clinical application of this result is that clustered colours appear to be no more significant than colours of arbitrary distribution within a lesion.     

Novice identification of melanoma: not quite as straightforward as the ABCDs

The "ABCD" mnemonic to assist non-experts' diagnosis of melanoma is widely promoted; however, there are good reasons to be sceptical about public education strategies based on analytical, rule-based approaches--such as ABCD (i.e. Asymmetry, Border Irregularity, Colour Uniformity and Diameter). Evidence suggests that accurate diagnosis of skin lesions is achieved predominately through non-analytical pattern recognition (via training examples) and not by rule-based algorithms. If the ABCD are to function as a useful public education tool they must be used reliably by untrained novices, with low inter-observer and intra-diagnosis variation, but with maximal inter-diagnosis differences. The three subjective properties (the ABCs of the ABCD) were investigated experimentally: 33 laypersons scored 40 randomly selected lesions (10 lesions × 4 diagnoses: benign naevi, dysplastic naevi, melanomas, seborrhoeic keratoses) for the three properties on visual analogue scales. The results (n = 3,960) suggest that novices cannot use the ABCs reliably to discern benign from malignant lesions.     

Mobile teledermatology for skin tumour screening: diagnostic accuracy of clinical and dermoscopic image tele-evaluation using cellular phones

Background The ability to diagnose malignant skin tumours accurately and to distinguish them from benign lesions is vital in ensuring appropriate patient management. Little is known about the effects of mobile teledermatology services on diagnostic accuracy and their appropriateness for skin tumour surveillance. Objectives To evaluate the diagnostic accuracy of clinical and dermoscopic image tele‐evaluation for mobile skin tumour screening. Methods Over a 3‐month period up to three clinical and dermoscopic images were obtained of 113 skin tumours from 88 patients using a mobile phone camera. Dermoscopic images were taken with a dermatoscope applied to the camera lens. Clinical and dermoscopic images of each lesion together with clinical information were separately teletransmitted for decision‐making. Results were compared with those obtained by face‐to‐face examination and histopathology as the gold standard. Results A total of 322 clinical and 278 dermoscopic images were acquired; two (1%) clinical and 18 (6%) dermoscopic pictures were inadequate for decision‐making. After excluding inadequate images, the majority of which were dermoscopic pictures, only 104 of the 113 skin tumours from 80 of 88 patients could be tele‐evaluated. Among these 104 lesions, 25 (24%) benign nonmelanocytic, 15 (14%) benign melanocytic, 58 (56%) malignant nonmelanocytic and six (6%) malignant melanocytic lesions were identified. Clinical and dermoscopic tele‐evaluations demonstrated strong concordance with the gold standard (κ = 0·84 for each) and similar high sensitivity and specificity for all diagnostic categories. With regard to the detailed diagnoses, clinical image tele‐evaluation was superior to teledermoscopy resulting in 16 vs. 22 discordant cases. Conclusions Clinical image tele‐evaluation might be the method of choice for mobile tumour screening.     

Colour histogram analysis for melanoma discrimination in clinical images

Background: Malignant melanoma, the most deadly form of skin cancer, has a good prognosis if treated in the curable early stages. Colour provides critical discriminating information for the diagnosis of malignant melanoma.
Methods: This research introduces a three-dimensional relative colour histogram analysis technique to identify colours characteristic of melanomas and then applies these 'melanoma colours' to differentiate benign skin lesions from melanomas. The relative colour of a skin lesion is determined based on subtracting a representative colour of the surrounding skin from each lesion pixel. A colour mapping for 'melanoma colours' is determined using a training set of images. A percent melanoma colour feature, defined as the percentage of the lesion pixels that are melanoma colours, is used for discriminating melanomas from benign lesions. The technique is evaluated using a clinical image data set of 129 malignant melanomas and 129 benign lesions consisting of 40 seborrheic keratoses and 89 nevocellular nevi.
Results: Using the percent melanoma colour feature for discrimination, experimental results yield correct melanoma and benign lesion discrimination rates of 84.3 and 83.0%, respectively.
Conclusions: The results presented in this work suggest that lesion colour in clinical images is strongly related to the presence of melanoma in that lesion. However, colour information should be combined with other information in order to further reduce the false negative and false positive rates.     

A simple scoring system for the diagnosis of palmo‐plantar pigmented skin lesions by digital dermoscopy analysis

Background Many research groups have recently developed equipments and statistical methods enabling pattern classification of pigmented skin lesions. To differentiate between benign and malignant ones, the mathematical extraction of digital patterns together with the use of appropriate statistical approaches is a challenging task. Objective To design a simple scoring model that provides accurate classification of benign and malignant palmo‐plantar pigmented skin lesions, by evaluation of parameters obtained by digital dermoscopy analysis (DDA). Patients and Methods In the present study we used a digital dermoscopy analyser to evaluate a series of 445 palmo‐plantar melanocytic skin lesion images (25 melanomas 420 nevi). Area under the receiver operator curve, sensitivity and specificity were calculated to evaluate the diagnostic performance of our scoring model for the differentiation of benign and malignant palmo‐plantar melanocytic lesions. Results Model performance reached a very high value (0.983). The DDA parameters selected by the model that proved statistically significant were: area, peripheral dark regions, total imbalance of colours, entropy, dark area and red and blue multicomponent. When all seven model variables were used in a multivariate mode, setting sensitivity at 100% to avoid false negatives, we estimated a minimum specificity of about 80%. Conclusions Simplicity of use and effectiveness of implementation are important requirements for the success of quantitative methods in routine clinical practice. Scoring systems meet these requirements. Their outcomes are accessible in real time without the use of any data processing system, thus allowing decisions to be made quickly and effectively.