Early Detection of Malignant Melanoma: The Role of Physician Examination and Self-Examination of the Skin

Friedman, Robert J.; Rigel, Darrell S.; Kopf, Alfred W.

doi:10.3322/canjclin.35.3.130

Cited by 670 publications

(401 citation statements)

References 37 publications

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“…Clinical diagnosis of cutaneous melanoma is commonly based on the ABCD rule (Friedman et al, 1985), Menzies method (Menzies, 2001) and seven-point checklist (Liu et al, 2005). According to the above-cited methods, melanomas usually have differential structures, such pigmented networks, streaks or dots, giving clues about their melanocytic origin.…”

Section: Introductionmentioning

confidence: 99%

Reticular pattern detection in dermoscopy: an approach using Curvelet Transform

2016

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Introduction: Dermoscopy is a non-invasive in vivo imaging technique, used in dermatology in feature identification, among pigmented melanocytic neoplasms, from suspicious skin lesions. Often, in the skin exam is possible to ascertain markers, whose identification and proper characterization is difficult, even when it is used a magnifying lens and a source of light. Dermoscopic images are thus a challenging source of a wide range of digital features, frequently with clinical correlation. Among these markers, one of particular interest to diagnosis in skin evaluation is the reticular pattern. Methods: This paper presents a novel approach (avoiding pre-processing, e.g. segmentation and filtering) for reticular pattern detection in dermoscopic images, using texture spectral analysis. The proposed methodology involves a Curvelet Transform procedure to identify features. Results: Feature extraction is applied to identify a set of discriminant characteristics in the reticular pattern, and it is also employed in the automatic classification task. The results obtained are encouraging, presenting Sensitivity and Specificity of 82.35% and 76.79%, respectively. Conclusions: These results highlight the use of automatic classification, in the context of artificial intelligence, within a computer-aided diagnosis strategy, as a strong tool to help the human decision making task in clinical practice. Moreover, the results were obtained using images from three different sources, without previous lesion segmentation, achieving to a rapid, robust and low complexity methodology. These properties boost the presented approach to be easily used in clinical practice as an aid to the diagnostic process.

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Section: Introductionmentioning

confidence: 99%

Reticular pattern detection in dermoscopy: an approach using Curvelet Transform

2016

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“…Nowadays, the diagnosis of melanoma is based on the ABCD rule [1] which considers four clinical features commonly observed in this kind of tumour: asymmetry, border irregularity, colour variegation, and a diameter larger than 5 mm. Although most of melanomas are correctly diagnosed following this rule, a variable proportion of melanomas does not comply with these criteria.…”

Section: Introductionmentioning

confidence: 99%

Experiences Using Clustering and Generalizations for Knowledge Discovery in Melanomas Domain

Fornells

Armengol

Golobardes

et al. 2008

Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects

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Abstract. One of the main goals in prevention of cutaneous melanoma is early diagnosis and surgical excision. Dermatologists work in order to define the different skin lesion types based on dermatoscopic features to improve early detection. We propose a method called SOMEX with the aim of helping experts to improve the characterization of dermatoscopic melanoma types. SOMEX combines clustering and generalization to perform knowledge discovery. First, SOMEX uses Self-Organizing Maps to identify groups of similar melanoma. Second, SOMEX builds general descriptions of clusters applying the anti-unification concept. These descriptions can be interpreted as explanations of groups of melanomas. Experiments prove that explanations are very useful for experts to reconsider the characterization of melanoma classes.

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“…A significant number of malignant melanomas, especially early melanomas curable by excision, are not diagnosed correctly in the clinical setting [1][2][3][4]. The diagnostic sensitivity reported for unaided dermatologist observers ranges from a low of about 66% to about 81% [1][2][3]5].…”

Section: Introductionmentioning

confidence: 99%

“…Current melanoma detection techniques rely on simple rules based on the ABCD's of melanoma (Asymmetry, Border irregularity, Color variegation, Diameter greater than 6 mm or growing) [4]. Most melanomas have a diameter of more than 6 mm, although public awareness has enabled more small melanomas to be found.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images

Stanley

Moss

Stoecker

et al. 2003

Computerized Medical Imaging and Graphics

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A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermatology clinical images. The approach utilizes a fuzzy set for benign skin lesion color, and alpha-cut and support set cardinality for quantifying a fuzzy ratio skin lesion color feature. Skin lesion discrimination results are reported for the fuzzy ratio and fusion with a previously determined percent melanoma color feature over a data set of 258 clinical images. For the fusion technique, alpha-cuts for the fuzzy ratio can be chosen to recognize over 93.30% of melanomas with approximately 15.67% false positive lesions.

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Early Detection of Malignant Melanoma: The Role of Physician Examination and Self-Examination of the Skin

Cited by 670 publications

References 37 publications

Reticular pattern detection in dermoscopy: an approach using Curvelet Transform

Reticular pattern detection in dermoscopy: an approach using Curvelet Transform

Experiences Using Clustering and Generalizations for Knowledge Discovery in Melanomas Domain

A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images

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