Digital Dermoscopy Analysis for the Differentiation of Atypical Nevi and Early Melanoma

Andreassi, Lucio; Perotti, Roberto; Rubegni, Pietro; Burroni, Marco; Cevenini, Gabriele; Biagioli, Maurizio; Taddeucci, Paolo; Dell'Eva, Giordana; Barbini, Paolo

doi:10.1001/archderm.135.12.1459

Cited by 125 publications

(105 citation statements)

References 41 publications

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“…One of the most common approaches is to derive basic statistics of colour properties in RGB or other colour spaces. Mean, standard deviation and other simple measures are computed for colours occurring in the lesion and in the surrounding skin, providing colour-related quantitative parameters for subsequent lesion classification [9, 11, 12,25,26,27,28,29,30]. …”

Section: Discussionmentioning

confidence: 99%

“…However, dermoscopic techniques require formal training and skill in image interpretation by so-called pattern analysis [1,2,3]. To overcome the unavoidable subjectivity and variability in the interpretation of dermoscopic images [4], programs for image analysis, enabling the numerical description of the morphology of pigmented skin lesion images, have been developed [5,6,7,8,9,10,11,12,13]. These programs assess the geometry, texture, pigmentation and the colours of the lesion in an objective and reproducible way, providing mathematical parameters to be used for lesion discrimination.…”

Section: Introductionmentioning

confidence: 99%

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Colour Clusters for Computer Diagnosis of Melanocytic Lesions

Seidenari

Grana²,

Pellacani³

2007

Dermatology

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Background: To overcome subjectivity and variability in the interpretation of dermoscopic images, image analysis programs, enabling the numerical description of melanocytic lesion images, have been developed. Objectives: Our aim was to assess a method for the description of colours in melanocytic lesion images, based on the subdivision of image colours into red, green and blue clusters. Methods: Melanomas and naevi of the test set were described by means of 23 colour clusters previously selected by a training set comprising 369 melanocytic lesion images. The diagnostic performance obtained by this automated method was compared to sensitivity and specificity of diagnosis of 4 dermatologists. Results: Colour cluster values significantly differed between melanomas and naevi. Moreover, sensitivity and specificity values of computer diagnosis were similar to those achieved by the dermatologists. Conclusion: Our image analysis program based on the assessment of one single parameter has the diagnostic accuracy of dermatologists employing dermoscopy on a regular basis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Colour Clusters for Computer Diagnosis of Melanocytic Lesions

Seidenari

Grana²,

Pellacani³

2007

Dermatology

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“…Analysis of various analytic and clinical features by Rubegni et al, showed that the single most significant feature was red asymmetry [15]. This research group used variables that capture the number and types of color elements within a lesion, to represent the chaotic pattern of color present within a lesion, related to the "color islands" previously described by this group [16]. Other color descriptors used to detect melanoma include variation of hues [17], analytical color techniques for detecting color variegation [18], RGB color channel statistical parameters [19][20][21], spherical color coordinates and (L, a*, b*) color coordinate features [22], percentage of the skin lesion containing absolute shades of reddish, bluish, grayish and blackish areas and the number of those color shades present within the skin lesion [23].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Color Clustering for Melanoma Diagnosis in Dermoscopy Images

et al. 2017

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A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermoscopy images. The approach extends previous research for utilizing a fuzzy set for skin lesion color for a specified class of skin lesions, using alpha-cut and support set cardinality for quantifying a fuzzy ratio skin lesion color feature. Skin lesion discrimination results are reported for the fuzzy clustering ratio over different regions of the lesion over a data set of 517 dermoscopy images consisting of 175 invasive melanomas and 342 benign lesions. Experimental results show that the fuzzy clustering ratio applied over an eight-connected neighborhood on the outer 25% of the skin lesion with an alpha-cut of 0.08 can recognize 92.6% of melanomas with approximately 13.5% false positive lesions. These results show the critical importance of colors in the lesion periphery. Our fuzzy logic-based description of lesion colors offers relevance to clinical descriptions of malignant melanoma.

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“…Digital image processing applied to dermoscopic images is an important tool to improve naked eye evaluation, increasing sensibility and specificity to diagnose (Andreassi et al, 1999;Menzies et al, 1996;Piccolo et al, 2002). The use of image processing techniques enabled quantification, pre-processing (enhancing contrast, alignment, among others), eliminate common artifacts (Abbas et al, 2011;Afonso and Silveira, 2012;Fonseca-Pinto et al, 2010) and extract quantitative features to classify structures (Andreassi et al, 1999;Soyer et al, 1995) to establish a measure of malignancy associated to the lesion, as proposed in (Abbas et al, 2013;Argenziano et al, 2011;Blum et al, 2003;Piccolo et al, 2014;Şavk et al, 2004;Zalaudek et al, 2006a). These techniques also allow the use of automated classification of skin lesion, which is a valuable help to clinical practice (Cheng et al, 2013;Liu et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

On the geometric modulation of skin lesion growth: a mathematical model for melanoma

et al. 2016

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Introduction: Early detection of suspicious skin lesions is critical to prevent skin malignancies, particularly the melanoma, which is the most dangerous form of human skin cancer. In the last decade, image processing techniques have been an increasingly important tool for early detection and mathematical models play a relevant role in mapping the progression of lesions. Methods: This work presents an algorithm to describe the evolution of the border of the skin lesion based on two main measurable markers: the symmetry and the geometric growth path of the lesion. The proposed methodology involves two dermoscopic images of the same melanocytic lesion obtained at different moments in time. By applying a mathematical model based on planar linear transformations, measurable parameters related to symmetry and growth are extracted. Results: With this information one may compare the actual evolution in the lesion with the outcomes from the geometric model. First, this method was tested on predefined images whose growth was controlled and the symmetry known which were used for validation. Then the methodology was tested in real dermoscopic melanoma images in which the parameters of the mathematical model revealed symmetry and growth rates consistent with a typical melanoma behavior. Conclusions: The method developed proved to show very accurate information about the target growth markers (variation on the growth along the border, the deformation and the symmetry of the lesion trough the time). All the results, validated by the expected phantom outputs, were similar to the ones on the real images.

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Digital Dermoscopy Analysis for the Differentiation of Atypical Nevi and Early Melanoma

Cited by 125 publications

References 41 publications

Colour Clusters for Computer Diagnosis of Melanocytic Lesions

Colour Clusters for Computer Diagnosis of Melanocytic Lesions

Fuzzy Color Clustering for Melanoma Diagnosis in Dermoscopy Images

On the geometric modulation of skin lesion growth: a mathematical model for melanoma

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