Our results underline that teledermoscopy of 'pink' lesions does not provide a similar degree of diagnostic accuracy as otherwise in face-to-face diagnosis perhaps due to the absence of typical criteria. Atypical skin lesions are characterized by the absence of typical dermoscopic patterns, and their teleconsultation does not always increase the diagnostic accuracy.
Epiluminescence microscopy (ELM) is a non invasive technique used to enhance visualization of microscopic structures of pigmented lesions for the early detection of melanoma. The 7 point check list is a diagnostic method that requires the identification of only seven dermoscopic criteria, defining the image through the use of algorithms. This paper describes an experimental automated diagnosis set up of melanocytic skin lesions through an image processing methodology focused on finding the presence of different epiluminescence parameters. In this paper the image processing set up allows the automatic detection of some specific dermoscopic criteria. We analyze the blue whitish veil, the regression, and the irregular streaks. The procedure developed was tested by considering a set of about 200 ELM images. A good concordance between ELM 7 point checklist parameters detected and the new method of image processing was achieved by kappa analysis. Although ELM doesn't substitute histological evaluation, it could be a reliable instrument to enhance clinical accuracy of skin pigmented lesions diagnosis.
Psoriasis is a chronic, inflammatory, and relapsing disease which affects 1% of children and adolescents and whose onset before 20 years of age occurs in 35%-50% of patients. 1 Even in childhood, moderate-to-severe psoriasis is associated with increased incidence of multiple comorbidities such as psoriatic arthritis, obesity, diabetes, inflammatory bowel diseases, and reduced health related quality of
Early detection of melanoma is one of the greatest challenges of dermatologic practice today. A new diagnostic method, the “ELM 7 point checklist”, defines a set of seven features, based on colour and texture parameters, which describe the malignancy of a lesion. It has been presented as faster and with the same accuracy than the traditional ABCD criteria in the diagnosis of melanoma. In this paper a new system for automated diagnosis of melanocytic skin lesions, based on ELM 7 point checklist, is introduced
This paper deals with ELM image processing for automatic analysis of pigmented skin lesions which represents one of the greatest challenges of dermatologic practice today. The "ELM 7 point checklist" defines a set of seven features, based on colour and texture parameters, which describe the malignancy of a lesion. It has been revealed as faster and with the same accuracy than the traditional ABCD criteria in the diagnosis of melanoma. A preliminary approach to the automated diagnosis of melanocytic skin lesions, based on ELM 7 point checklist is proposed. In particular, the image processing algorithms and classification techniques involved in the automatic detection of the occurrence of two criteria (Blue-whitish Veil and Regression structures) are introduced and the experimental results are reported
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