Pigment distribution in melanocytic lesion images: a digital parameter to be employed for computer‐aided diagnosis

Abstract

This original evaluation method for digital pigment distribution, based on mathematical description and comparison of colours in different image blocks, provides numerical parameters to be implemented in image analysis programs for computer-aided MM diagnosis.

Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma

“…Forty-three studies, 3 These included two reviews, 3,17 one randomized controlled trial (RCT) 12 and one study reported as an abstract. 83 Reasons for exclusion were: lack of information to calculate diagnostic accuracy of the index test (13 studies 8,12,50-60 ); selection bias -selective inclusion of the lesions (11 studies [61][62][63][64][65][66][67]69,78,82,84 ) and selective excision of the lesions (one study 84 ); histology not available (five studies 9,11,[70][71][72] ); test performed by experts and nonexperts but data not reported separately (two studies 10,13 ); study conducted as a training exercise (three studies [74][75][76] ); discussion of different diagnostic tools or parameters for melanoma diagnosis than stipulated in the review (four studies 68,77,79,80 ); use of the index test as a tool for follow-up of the atypical skin lesions rather than for diagnosis; 73 and discussion about limitations of dermoscopy. 81…”

Quantifying dyspigmentation in facial skin ageing: an explorative study

“…Image-processing techniques are widely used in dermatology, covering the analysis of histologic sections [39] to the quantification of facial erythema [40,41]. The investigation of pigmented lesions is of particular interest; however, it is accompanied by difficulties in finding the optimal threshold in computerized algorithms [42,43]. A similar image-processing algorithm was proposed previously to quantify hyperpigmented spots of the facial skin [44].…”

Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis

“…One of the simplest approaches is to derive basic descriptive statistics from the distribution of colour primaries inside the lesion. Seidenari et al [70, 71] divided each image into nonoverlapping blocks where each block was represented by the average colour of its contained pixels. This was followed by computing colour difference between blocks using the Euclidean distance in the RGB colour space (each colour block is compared with every other block in the image and not only to its corresponding one about the axis of symmetry).…”