Dermoscopy of pigmented lesions

University of Miami, كورال غيبلز، فلوريدا, Florida, United States
Journal of the American Academy of Dermatology (Impact Factor: 5). 02/2005; 52(1):109-21. DOI: 10.1016/j.jaad.2001.11.001
Source: PubMed

ABSTRACT Dermoscopy is an in vivo method for the early diagnosis of malignant melanoma and the differential diagnosis of pigmented lesions of the skin. It has been shown to increase diagnostic accuracy over clinical visual inspection in the hands of experienced physicians. This article is a review of the principles of dermoscopy as well as recent technological developments.

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    • "São usadas sete características da imagem divididas em critérios major (com peso de 2) e minor (com peso de 1) tendo sendo esta soma o valor máximo de 7. Se forem obtidos menos de 3 pontos a lesão é considerada benigna, no caso de serem 3 ou mais pontos a lesão tem uma probabilidade elevada de ser um melanoma. [5] [6] [7]. Um exemplo desta classificação encontram-se na Figura 8, na qual podemos observar a pontuação máx- ima. "
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    ABSTRACT: Artigo de revisão sobre cancro da pele e investigação biomédica ao nível do processamento de imagem digital.
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    • "Generally, there are 2 general clinical diagnosis approaches for malignant melanoma which are both based of characteristics such as color, shape, dimension and texture: ABCD rule and 7-points checklist [7], [8]. Clinical diagnosis approaches also depend on the features like asymmetry, border irregularity , shape and dimension properties. "
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    ABSTRACT: Accurate identification and extraction of region of interest (ROI) in dermoscopy images play crucial role in diagno-sis, and treatment of melanoma and other skin diseases. Human interpretation of dermoscopy images is not only tedious and time consuming task but also subjective. This fact has attracted numerous attentions for developing automated assessment tools. In this paper, we present lesion detection schemes in dermoscopy images on mobile platforms. The systems are based on density based clustering (DBSCAN) and fuzzy c-means (FCM) clustering and developed for Windows Phone and Android environments. We tested the systems on dermoscopy images and ROIs are successfully extracted. The proposed systems may improve the management of melanoma by providing automatic early moni-toring of skin lesions that will assist clinical investigation.
    IEEE International Symposium on Medical Measurement and Applications; 06/2014
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    • "First, of all local dermoscopic structures, atypical pigment network is most highly correlated with malignant melanoma [16]. Second, the identification of a pigment network is the first stage of the two-step algorithm for differentiating between melanocytic and nonmelanocytic lesions [17]. If a lesion can be determined to be nonmelanocytic, then the diagnosis of melanoma can be ruled out entirely. "
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    ABSTRACT: We present a general model using supervised learning and MAP estimation that is capable of performing many common tasks in automated skin lesion diagnosis. We apply our model to segment skin lesions, detect occluding hair, and identify the dermoscopic structure pigment network. Quantitative results are presented for segmentation and hair detection and are competitive when compared to other specialized methods. Additionally, we leverage the probabilistic nature of the model to produce receiver operating characteristic curves, show compelling visualizations of pigment networks, and provide confidence intervals on segmentations.
    IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society 05/2011; 15(4):622-9. DOI:10.1109/TITB.2011.2150758 · 2.07 Impact Factor
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