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.     

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.     

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.     

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.     

Application of optical coherence tomography in non-invasive characterization of skin vascular lesions

Background: Optical coherence tomography (OCT) is a new non-invasive approach for real-time in vivo tissue characterization. A promising use of OCT can be the assessment of the architecture of lesions with some degree of inhomogeneities, such as vascular lesions. Knowledge of the size and depth of the vascular structures can be useful for the diagnosis and for choosing the best treatment.
Objective: The purpose of this study was to investigate a series of vascular lesions by means of OCT in order to obtain new insights into the non-invasive, pre-operative analysis of these lesions.
Methods: Seven vascular lesions were included in the study. Histopathological diagnosis showed two haemangiomas and one haemolymphangioma; the remaining four cases were classified as haemangiomas on the basis of their clinical appearance.
Results: In all lesions, OCT analysis was able to visualize different areas of the lesion from the horny layer to the dermis showing a clear image of the vascular proliferation. Specifically, oval to roundish signal-poor areas sharply demarcated by a surrounding signal-rich layer were observed in good correlation with histopathology.
Conclusion: The analysis of vascular lesions by OCT provides a new insight into non-invasive diagnosis and can be helpful in the selection of the most appropriate treatment.     

A novel case of nocardiosis with skin lesion due to Nocardia araoensis

Nocardiosis is caused by gram-positive aerobic actinomycetes that live in soil and are known to be responsible for opportunistic infections. The condition mostly affects the lung, brain or skin. Here, we present a 24-year-old Japanese woman who had had systemic lupus erythematosus since the age of 20 years, and lupus nephritis since the age of 23 years. She developed cutaneous lymph duct-type nocardiosis due to Nocardia araoensis while on immunosuppressant therapy. The patient had cutaneous findings from the right inguinal region to the right lower thigh and did not have lesions on the rest of the body. Minocycline and co-trimoxazole were co-administrated, and her condition improved. To our knowledge, this is the first case in which N. araoensis was detected by analysis on rRNA base sequence in skin lesions.     

Successful triage of patients referred to a skin lesion clinic using teledermoscopy (IMAGE IT trial)

Background Teledermatology is a rapidly growing field with studies showing high diagnostic accuracy when compared with face‐to‐face diagnosis. Teledermoscopy involves the use of epiluminescence microscopy to increase diagnostic accuracy. The utility of teledermoscopy as a triage tool has not been established. Objectives To assess teledermoscopy as a triage tool for a hospital skin lesion clinic. Methods Patients referred to a dermatology skin lesion clinic were recruited. Digital and dermoscopic photographs were taken of skin lesions of concern and the patients were then seen independently face‐to‐face by two out of three dermatologists. The digital images were evaluated 4 weeks later, as a teledermoscopy consultation, by two of these dermatologists. The diagnosis and management from both types of consultation were compared. Results Two hundred patients with a total of 491 lesions were seen. There was excellent agreement between teledermoscopy and face‐to‐face diagnosis with only 12·3% of lesions having disparate diagnoses of clinical significance. Twelve of 491 (2·4%) lesions appeared to have been under‐reported by teledermoscopy when compared with face‐to‐face diagnosis. However, when histopathology became available, only one malignant lesion had been missed (a basal cell carcinoma diagnosed as solar keratosis) by teledermoscopy. Teledermoscopy approximated 100% sensitivity and 90% specificity for detecting melanoma and nonmelanoma skin cancers. Importantly, 74% of all lesions were determined to be manageable by the general practitioner without needing to be seen face‐to‐face by a dermatologist. Conclusions This use of teledermoscopy as a triage tool offers the potential to shorten waiting lists and thus improve healthcare access and delivery.     

An image‐processing analysis of skin textures

Background: This paper discusses an image‐processing method applied to skin texture analysis. Considering that the characterisation of human skin texture is a task approached only recently by image processing, our goal is to lay out the benefits of this technique for quantitative evaluations of skin features and localisation of defects. Methods: We propose a method based on a statistical approach to image pattern recognition. The results of our statistical calculations on the grey‐tone distributions of the images are proposed in specific diagrams, the coherence length diagrams. Results: Using the coherence length diagrams, we were able to determine grain size and anisotropy of skin textures. Maps showing the localisation of defects are also proposed. Conclusion: According to the chosen statistical parameters of grey‐tone distribution, several procedures to defect detection can be proposed. Here, we follow a comparison of the local coherence lengths with their average values. More sophisticated procedures, suggested by clinical experience, can be used to improve the image processing.     

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.     

Graphic analysis of the relationship between skin colour change and variations in the amounts of melanin and haemoglobin

Background/aims The L*a*b* coordinate is the most commonly used colour system to measure skin colour in dermatology and cosmetology. In this system, a* and L* are often used for quantification of the degrees of erythema and pigmentation. The aim of this study was to examine whether a* and L* can be used as specific scales to indicate the amount of haemoglobin and melanin, respectively, in the skin.
Methods The a* and L* values were examined with a reflectance spectrometer in various skin conditions or lesions caused by a change in the amount of either melanin or haemoglobin, i.e. vitiligo, ultraviolet-induced pigmentation (PG), erythema resulting from slapping (ER), corticosteroid-induced blanching, erythema due to stasis by arm lowering, and a combination of PG and ER. The differences in values between the test sites and the adjacent normal skin, Δa* and ΔL*, were plotted on the Δa*–ΔL* plane and analysed statistically and geometrically.
Results L* depended substantially not only on melanin but also on haemoglobin, especially if the oxygen saturation level was expected to be low. a* was also influenced by melanin. The results of graphic analysis indicated that a linear transformation of (Δa*, ΔL*) into (ΔHb = 1.68 Δa* + 0.60 ΔL*, ΔMel =−1.06 Δa*–1.44 ΔL*) was suitable for separately estimating the change in the amount of haemoglobin (ΔHb) and in that of melanin (ΔMel).
Conclusion The results of this study may be of value for understanding the relationship between colour coordinates of the skin and the quantities of haemoglobin and melanin, and may be of use when pigmented lesions of the face are monitored by tristimulus colourimetry, as facial skin colour is affected considerably by the rich and easily variable cutaneous blood flow.