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.     

Skin pattern analysis for lesion classification using local isotropy

Background/purpose: The observation that skin pattern tends to be disrupted by malignant skin lesions, but not by benign ones 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 intensity, was encouraging. The aim of this paper is to investigate the possibility of extracting new features using local isotropy metrics to quantify the skin pattern disruption. Methods: The skin pattern was extracted from WLC skin images by high‐pass filtering. A local tensor matrix was computed. The local isotropy was measured by the condition number of the local tensor matrix. The difference of this measure over the lesion and normal skin areas, combined with the local line direction and the ABCD features, was used as a lesion classifier. Results: A set of images of malignant melanoma and benign naevi was analysed. A one‐dimensional scatter plot showed the potential of a local isotropy metric, showing an area of 0.70 under the receiver operating characteristic (ROC) curve. A two‐dimensional scatter plot, combined with the local line direction, indicated enhancement of the classification performance, showing an area of 0.89 under the ROC curve. A three‐dimensional scatter plot combined with the local line direction and the ABCD features, using principal component analysis, demonstrated excellent separation of benign and malignant lesions. An ROC plot for this case enclosed an area of 0.96. Conclusion: The experimental results show that the local isotropy metric has a potential to increase lesion classifier accuracy. Combined with the local line direction and the ABCD features, it is very promising as a method to distinguish malignant melanoma from benign lesions.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

Pre-diagnostic digital imaging prediction model to discriminate between malignant melanoma and benign pigmented skin lesion

Background: Malignant cutaneous melanoma is the most deadly form of skin cancer with an increasing incidence over the past decades. The final diagnosis provided is typically based on a biopsy of the skin lesion under consideration. To assist the naked-eye examination and decision on whether or not a biopsy is necessary, digital image processing techniques provide promising results.
Hypothesis and aims: The hypothesis of this study was that a computer-aided assessment tool could assist the evaluation of a pigmented skin lesion. Hence, the overall aim was to discriminate between malignant and benign pigmented skin lesions using digital image processing.
Methods: Discriminating algorithms utilizing novel well-established morphological operations and methods were constructed. The algorithms were implemented utilizing graphical programming (LabVIEW Vision). Verification was performed with reference to an image database consisting of 97 pigmented skin lesion pictures of various resolutions and light distributions. The outcome of the algorithms was analysed statistically with MATLAB and a prediction model was constructed.
Results/Conclusion: The prediction model evaluates pigmented skin lesions with regards to the overall shape, border and colour distribution with a total of nine different discriminating parameters. The prediction model outputs an index score, and by using the optimal threshold value, a diagnostic accuracy of 77% in discriminating between malignant and benign skin lesions was obtained. This is an improvement compared with the naked-eye analysis performed by professionals, rendering the system a significant assistance in detecting malignant cutaneous melanoma.     

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 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.     

Malignant blue nevus with lymph node metastases

Background:  Malignant blue nevi arise within cellular blue nevi and contain atypical mitoses, necrosis, nuclear pleomorphism and prominent nucleoli. Malignant blue nevus has been described as a distinct identity, a rare form of malignant melanoma, and a misdiagnosed melanoma.
Methods:  We present a patient with metastatic malignant blue nevus and studies on the histopathologic, immunohistochemical, and molecular features of the neoplasm.
Results:  Histology showed a malignant blue nevus arising in a combined intradermal and cellular blue nevus. CD117 (c-kit) staining showed diffuse cytoplasmic expression within the cellular blue nevus, decreased staining in the malignant component, and variable positivity within the lymph node metastases. Molecular loss of heterozygosity analysis showed different allelic patterns at the hOGG-1 locus between the melanoma and control skin specimens with a varying heterozygous allelic pattern in both the benign and malignant blue nevus.
Conclusions:  Our case of malignant blue nevus with lymph node metastasis involved mutation of the hOGG-1 DNA repair gene. CD117 showed decreased staining of the primary malignant blue nevus with marked upregulation in the metastatic lesion, unlike most metastatic melanomas. Further study is needed to determine if hOGG-1 mutation or c-kit upregulation play a role in the pathogenesis of dendritic melanocytic lesions (either benign or malignant).     

Who sends what: a comparison of dermatopathology referrals from dermatologists, pathologists and dermatopathologists

Background:  Dermatopathologists, dermatologists and pathologists interpret skin pathology specimens.
Objective:  To examine dermatopathology referral patterns of dermatologists, pathologists and dermatopathologists.
Methods:  We retrospectively reviewed diagnoses rendered by one dermatopathologist to 916 primary interpretation cases (543 from university dermatologists and 373 from private practice dermatologists) and 517 consultations (450 from dermatologists, 52 from pathologists and 15 from dermatopathologists). Each diagnosis was assigned into one of six categories. Chi-square tests were used to compare referral types pairwise and correspondence analysis was performed.
Results:  All profile comparisons tested significantly from each other (p-value < 0.01) except the comparison between dermatopathologists and pathologists. Correspondence analysis suggested that consultation profile of dermatopathologists was most dissimilar from other profiles and tended to associate more with the presence of malignant and benign melanocytic referral types. Referral pattern of pathologists was more similar to that of dermatologists who interpret skin pathology specimens than that of dermatopathologists.
Limitations:  Small sample size, referral bias, difficulty classifying certain lesions.
Conclusions:  Referral pattern of dermatopathologists was most dissimilar from other profiles and tended to associate more with malignant and benign melanocytic referral types. Referral pattern of pathologists was more similar to that of dermatologists who interpret skin pathology specimens than that of dermatopathologists.     

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.     

Expression of heat shock protein 105 and 70 in malignant melanoma and benign melanocytic nevi

Background:  Heat shock proteins (HSPs) restore immature proteins or denatured proteins, thus protecting cells. Also, the expression of some HSPs is elevated substantially in malignant tumors, but the expression of HSPs in association with melanoma has yet to be studied. Therefore, we examined the expression patterns of HSP 70 and 105 in melanoma, benign melanocytic nevi and normal human skin.
Methods:  Two specimens of malignant melanoma, two of benign melanocytic nevi and six of normal human skin were analyzed using Western blot analysis for expression of HSP 70 and 105. In another set, 16 specimens of malignant melanoma, 24 of benign melanocytic nevi and eight of normal human skin were analyzed for the expression of HSP 105 using immunohistochemical studies.
Results:  The Western blot analysis showed that HSP 70 was overexpressed in all three types. But, the HSP 105 was hardly expressed in normal human skin and benign melanocytic nevi. However, in malignant melanoma, the HSP 105 was overexpressed, and immunohistochemical examination of HSP 105 showed a result similar to that of Western blot analysis.
Conclusions:  In our study, HSP 105 is thought to be a more relevant tumor-associated antigen in malignant melanoma than is HSP 70.     

Optical transfer diagnosis of pigmented lesions: a pilot study

Objective: To evaluate the potential of a novel imaging technology, optical transfer diagnosis (OTD), for differentiation of benign from malignant pigmented melanocytic lesions.
Design: Patients with pigmented lesions suspicious for melanoma were referred for OTD. After scanning, lesions were biopsied for histopathologic examination, each by two separate dermatopathologists. To create morphologic–physiologic maps, the imaging system used the morphologic and physiologic parameters derived from prediction models of light absorption and scattering by chromophores such as hemoglobin, keratin, and melanin at different epidermal and dermal depths. The relative entropies were analyzed for output prediction of malignancy vs. nonmalignancy.
Setting: General dermatology clinic in a tertiary care academic medical center.
Patients: Fifty patients with suspected melanoma.
Intervention: OTD of pigmented lesions suspicious for melanoma, followed by biopsies for histopathologic examination.
Main outcome measures: Histopathologic confirmation of malignant lesions identified by OTD as melanoma.
Results: Sixty-three pigmented suspicious lesions were scanned before being biopsied for histopathologic examination by the two dermatopathologists. Of the 63 lesions, five were identified as melanoma and 58 were found to be benign (including three seborrheic keratoses and 55 melanocytic nevi). OTD was able to identify the malignant lesions with 100% sensitivity and 94.8–96.6% specificity.
Conclusions: Further study is indicated, but this technology is a promising adjunct to clinical skin cancer screening. Additionally, if the physiologic prediction models can be validated, OTD may facilitate the noninvasive study of some aspects of cutaneous physiology.     

Towards non-invasive screening of skin lesions by near-infrared spectroscopy

A noninvasive tool for skin tumor diagnosis would be a useful clinical adjunct. The purpose of this study was to determine whether near-infrared spectroscopy can be used to noninvasively characterize skin lesions. In vivo visible- and near-infrared spectra (400--2500 nm) of skin neoplasms (actinic keratoses, basal cell carcinomas, banal common acquired melanocytic nevi, dysplastic melanocytic nevi, actinic lentigines, and seborrheic keratoses) were collected by placing a fiberoptic probe on the skin. Paired t tests, repeated measures analysis of variance and linear discriminant analysis were used to determine whether significant spectral differences existed and whether spectra could be classified according to lesion type. Paired t tests showed significant differences (p < 0.05) between normal skin and skin lesions in several areas of the near-infrared spectrum. In addition, significant differences were found between the lesion groups by analysis of variance. Linear discriminant analysis classified spectra from benign lesions compared with premalignant or malignant lesions with high accuracy. Near-infrared spectroscopy is a promising noninvasive technique for the screening of skin 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.     

Electrical impedance scanning: a new approach to skin cancer diagnosis

Background/aims: Skin cancer diagnosis depends, to a great extent, on visual inspection and histopathological examination of excised tissues. The aim of this study is to evaluate the ability of electrical impedance scanning to differentiate between benign and malignant skin lesions.
Methods: A preclinical study was conducted on 40 nude mice injected subcutaneously with a human melanoma strain. Impedance measurements were recorded every week to correlate electrical changes with tumor growth and histological findings. A clinical study was also performed on 178 human suspicious skin lesions before excision. The impedance measurements were correlated to the histopathological results.
Results: Normalized conductivity and capacitance, recorded on growing skin tumors in nude mice, were shown to change relative to lesion size. Necrosis, present in most of the larger lesions, was associated with a decrease in the electrical conductivity. Similar electrical parameters were used to classify human melanoma lesions with 92% sensitivity and 67% specificity. In addition, four out of five BCC lesions were correctly diagnosed. Moreover, dysplastic lesions were diagnosed with 91% sensitivity and 59% specificity. For comparison, physicians diagnosed melanoma lesions with 75% sensitivity and 87% specificity and dysplastic lesions with 46% sensitivity and 80% specificity.
Conclusions: The animal study showed that electrical impedance measurements reflect morphological changes related to the growth of a cancerous skin lesion. These findings are in agreement with a preliminary clinical study. Electrical Impedance Scanning can therefore be considered as an objective and non-invasive tool for differentiation between benign and malignant skin lesions.