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Background Evolving dermoscopic terminology motivated us to initiate a new consensus. Objective We sought to establish a dictionary of standardized terms. Methods We reviewed the medical literature, conducted a survey, and convened a discussion among experts. Results Two competitive terminologies exist, a more metaphoric terminology that includes numerous terms and a descriptive terminology based on 5 basic terms. In a survey among members of the International Society of Dermoscopy (IDS) 23.5% (n = 201) participants preferentially use descriptive terminology, 20.1% (n = 172) use metaphoric terminology, and 484 (56.5%) use both. More participants who had been initially trained by metaphoric terminology prefer using descriptive terminology than vice versa (9.7% vs 2.6%, P < .001). Most new terms that were published since the last consensus conference in 2003 were unknown to the majority of the participants. There was uniform consensus that both terminologies are suitable, that metaphoric terms need definitions, that synonyms should be avoided, and that the creation of new metaphoric terms should be discouraged. The expert panel proposed a dictionary of standardized terms taking account of metaphoric and descriptive terms. Limitations A consensus seeks a workable compromise but does not guarantee its implementation. Conclusion The new consensus provides a revised framework of standardized terms to enhance the consistent use of dermoscopic terminology.

Patients with large congenital melanocytic nevi are at increased risk for developing melanomas. There is also a significant increased risk for developing NCM. The high incidence of CNS involvement may influence decisions concerning treatment of the large congenital melanocytic nevi.

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.

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