Skin texture parameters of the dorsal hand in evaluating skin aging in China

Background/purpose: There are various non‐invasive methods in skin morphology for assessing skin aging. The use of digital photography will make it easier and more convenient. In this study, we explored some skin texture parameters for evaluating skin aging using digital image processing. Methods: Two hundred and twenty‐eight subjects who lived in Sanya, China, were involved. Individual sun exposure history and other factors influencing skin aging were collected by a questionnaire. Meanwhile, we took photos of their dorsal hands. Skin images were graded according to the Beagley–Gibson system. These skin images were also processed using image analysis software. Five skin texture parameters, Angle Num., Angle Max., Angle Diff., Distance and Grids, were produced in reference to the Beagley–Gibson system. Results: All texture parameters were significantly associated with the Beagley–Gibson score. Among the parameters, the distance between primary lines (Distance) and the value of angle formed by intersection textures (Angle Max., Angle Diff.) were positively associated with the Beagley–Gibson score. However, there was a negative correlation between the number of grids (Grids), the number of angle (Angle Num.) and the Beagley–Gibson score. These texture parameters were also correlated with factors influencing skin aging such as sun exposure, age, smoking, drinking and body mass index. In multivariate analysis, Grids and Distance were mainly affected by age. But Angle Max. and Angle Diff. were mainly affected by sun exposure. Conclusion: It seemed that the skin surface morphologic parameters presented in our study reflect skin aging changes to some extent and could be used to describe skin aging using digital image processing.     

Automatic detection of basal cell carcinoma using telangiectasia analysis in dermoscopy skin lesion images

Background: Telangiectasia, dilated blood vessels near the surface of the skin of small, varying diameter, are critical dermoscopy structures used in the detection of basal cell carcinoma (BCC). Distinguishing these vessels from other telangiectasia, that are commonly found in sun‐damaged skin, is challenging. Methods: Image analysis techniques are investigated to find vessels structures in BCC automatically. The primary screen for vessels uses an optimized local color drop technique. A noise filter is developed to eliminate false‐positive structures, primarily bubbles, hair, and blotch and ulcer edges. From the telangiectasia mask containing candidate vessel‐like structures, shape, size and normalized count features are computed to facilitate the discrimination of benign skin lesions from BCCs with telangiectasia. Results: Experimental results yielded a diagnostic accuracy as high as 96.7% using a neural network classifier for a data set of 59 BCCs and 152 benign lesions for skin lesion discrimination based on features computed from the telangiectasia masks. Conclusion: In current clinical practice, it is possible to find smaller BCCs by dermoscopy than by clinical inspection. Although almost all of these small BCCs have telangiectasia, they can be short and thin. Normalization of lengths and areas helps to detect these smaller BCCs.     

Lesion border detection in dermoscopy images using dynamic programming

Background/purpose: Automated border detection is an important and challenging task in the computerized analysis of dermoscopy images. However, dermoscopic images often contain artifacts such as illumination, dermoscopic gel, and outline (hair, skin lines, ruler markings, and blood vessels). As a result, there is a need for robust methods to remove artifacts and detect lesion borders in dermoscopy images. Methods: This automated method consists of three main steps: (1) preprocessing, (2) edge candidate point detection, and (3) tumor outline delineation. First, algorithms to reduce artifacts were used. Second, a least‐squares method (LSM) was performed to acquire edge points. Third, dynamic programming (DP) technique was used to find the optimal boundary of the lesion. Statistical measures based on dermatologist‐drawn borders were utilized as ground‐truth to evaluate the performance of the proposed method. Results: The method is tested on a total of 240 dermoscopic images: 30 benign melanocytic, 50 malignant melanomas, 50 basal cell carcinomas, 20 Merkel cell carcinomas, 60 seborrheic keratosis, and 30 atypical naevi. We obtained mean border detection error of 8.6%, 5.04%, 9.0%, 7.02%, 2.01%, and 3.24%, respectively. Conclusions: The results demonstrate that border detection combined with artifact removal increases sensitivity and specificity for segmentation of lesions in dermoscopy images.     

Unsupervised border detection in dermoscopy images

BACKGROUND: As a result of the advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of skin cancer. Automated border detection is one of the most important steps in this procedure as the accuracy of the subsequent steps crucially depends on the accuracy of this step. METHODS: In this article, we present an unsupervised approach to border detection in dermoscopy skin lesion images based on a modified version of the JSEG algorithm. RESULTS: The method is tested on a set of 100 dermoscopy images. The border detection error is quantified by a metric that uses manually determined borders from a dermatologist as the ground truth. The results are compared with three other automated methods and manually determined borders by a second dermatologist. CONCLUSION: The results demonstrate that the presented method achieves both fast and accurate border detection in dermoscopy images.     

Detection of solid pigment in dermatoscopy images using texture analysis

Background/aims: Epiluminescence microscopy (ELM), also known as dermoscopy or dermatoscopy, is a non‐invasive, in vivo technique, that permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. ELM offers a completely new range of visual features. One such feature is the solid pigment, also called the blotchy pigment or dark structureless area. Our goal was to automatically detect this feature and determine whether its presence is useful in distinguishing benign from malignant pigmented lesions. Methods: Here, a texture‐based algorithm is developed for the detection of solid pigment. The factors d and a used in calculating neighboring gray level dependence matrix (NGLDM) numbers were chosen as optimum by experimentation. The algorithms are tested on a set of 37 images. A new index is presented for separation of benign and malignant lesions, based on the presence of solid pigment in the periphery. Results: The NGLDM large number emphasis N2 was satisfactory for the detection of the solid pigment. Nine lesions had solid pigment detected, and among our 37 lesions, no melanoma lacked solid pigment. The index for separation of benign and malignant lesions was applied to the nine lesions. We were able to separate the benign lesions with solid pigment from the malignant lesions with the exception of only one lesion, a Spitz nevus that mimicked a malignant melanoma. Conclusion: Texture methods may be useful in detecting important dermatoscopy features in digitized images and a new index may be useful in separating benign from malignant lesions. Testing on a larger set of lesions is needed before further conclusions can be made.     

Border detection in dermoscopy images using statistical region merging

Background: As a result of advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, because the accuracy of the subsequent steps crucially depends on it.
Methods: In this article, we present a fast and unsupervised approach to border detection in dermoscopy images of pigmented skin lesions based on the statistical region merging algorithm.
Results: The method is tested on a set of 90 dermoscopy images. The border detection error is quantified by a metric in which three sets of dermatologist-determined borders are used as the ground-truth. The proposed method is compared with four state-of-the-art automated methods (orientation-sensitive fuzzy c-means, dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method).
Conclusion: The results demonstrate that the method presented here achieves both fast and accurate border detection in dermoscopy images.     

Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes

BACKGROUND: Malignant melanoma has a good prognosis if treated early. Dermoscopy images of pigmented lesions are most commonly taken at x 10 magnification under lighting at a low angle of incidence while the skin is immersed in oil under a glass plate. Accurate skin lesion segmentation from the background skin is important because some of the features anticipated to be used for diagnosis deal with shape of the lesion and others deal with the color of the lesion compared with the color of the surrounding skin. METHODS: In this research, gradient vector flow (GVF) snakes are investigated to find the border of skin lesions in dermoscopy images. An automatic initialization method is introduced to make the skin lesion border determination process fully automated. RESULTS: Skin lesion segmentation results are presented for 70 benign and 30 melanoma skin lesion images for the GVF-based method and a color histogram analysis technique. The average errors obtained by the GVF-based method are lower for both the benign and melanoma image sets than for the color histogram analysis technique based on comparison with manually segmented lesions determined by a dermatologist. CONCLUSIONS: The experimental results for the GVF-based method demonstrate promise as an automated technique for skin lesion segmentation in dermoscopy images.     

741例皮肤肿瘤皮肤镜图像分析

目的:评价皮肤镜在皮肤肿瘤筛检中的应用价值。方法:比较皮肤镜图像分析与病理确诊的符合率。结果:皮肤镜图像分析技术在诊断皮肤肿瘤方面与病理诊断的总体符合率达92.44%,其中恶性黑素瘤、基底细胞癌、脂溢性角化病的诊断符合率分别达82.61%、96%和93.26%。结论:皮肤镜图像分析技术在皮肤肿瘤筛检中具有较高的应用价值。     

Detection of asymmetric blotches (asymmetric structureless areas) in dermoscopy images of malignant melanoma using relative color

BACKGROUND: Dermoscopy, also known as dermatoscopy or epiluminescence microscopy (ELM), is a non-invasive, in vivo technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One prominent feature useful for melanoma detection in dermoscopy images is the asymmetric blotch (asymmetric structureless area). METHOD: Using both relative and absolute colors, blotches are detected in this research automatically by using thresholds in the red and green color planes. Several blotch indices are computed, including the scaled distance between the largest blotch centroid and the lesion centroid, ratio of total blotch areas to lesion area, ratio of largest blotch area to lesion area, total number of blotches, size of largest blotch, and irregularity of largest blotch. RESULTS: The effectiveness of the absolute and relative color blotch features was examined for melanoma/benign lesion discrimination over a dermoscopy image set containing 165 melanomas (151 invasive melanomas and 14 melanomas in situ) and 347 benign lesions (124 nevocellular nevi without dysplasia and 223 dysplastic nevi) using a leave-one-out neural network approach. Receiver operating characteristic curve results are shown, highlighting the sensitivity and specificity of melanoma detection. Statistical analysis of the blotch features are also presented. CONCLUSION: Neural network and statistical analysis showed that the blotch detection method was somewhat more effective using relative color than using absolute color. The relative-color blotch detection method gave a diagnostic accuracy of about 77%.     

Detection of atypical texture features in early malignant melanoma

Background: The presence of an atypical (irregular) pigment network (APN) can indicate a diagnosis of melanoma. This study sought to analyze the APN with texture measures.
Methods: For 106 dermoscopy images including 28 melanomas and 78 benign dysplastic nevi, the areas of APN were selected manually. Ten texture measures in the CVIPtools image analysis system were applied.
Results: Of the 10 texture measures used, correlation average provided the highest discrimination accuracy, an average of 95.4%. Discrimination of melanomas was optimal at a pixel distance of 20 for the 768 × 512 images, consistent with a melanocytic lesion texel size estimate of 4–5 texels per mm.
Conclusion: Texture analysis, in particular correlation average at an optimized pixel spacing, may afford automatic detection of an irregular pigment network in early malignant melanoma.