Automatic dirt trail analysis in dermoscopy images

Abstract

Basal cell carcinoma (BCC) is the most common cancer in the U.S. Dermatoscopes are devices used by physicians to facilitate the early detection of these cancers based on the identification of skin lesion structures often specific to BCCs. One new lesion structure, referred to as dirt trails, has the appearance of dark gray, brown or black dots and clods of varying sizes distributed in elongated clusters with indistinct borders, often appearing as curvilinear trails. In this research, we explore a dirt trail detection and analysis algorithm for extracting, measuring, and characterizing dirt trails based on size, distribution, and color in dermoscopic skin lesion images. These dirt trails are then used to automatically discriminate BCC from benign skin lesions. For an experimental data set of 35 BCC images with dirt trails and 79 benign lesion images, a neural network-based classifier achieved a 0.902 area under a receiver operating characteristic curve using a leave-one-out approach, demonstrating the potential of dirt trails for BCC lesion discrimination.

Analysis of clinical and dermoscopic features for basal cell carcinoma neural network classification

“…These have been incorporated into the BASAL acronym: Blue-gray ovoids and globules, Arborizing telangiectasia, Semitranslucency/Spoke wheel structures, Atraumatic ulcerations, and Leaf-like structures/dirt-trails (2). Figure 1 While there have been numerous studies based on dermoscopic image feature analysis for pigmented lesion discrimination, few studies have specifically addressed BCC versus benign lesion discrimination by a classifier (3)(4)(5)(6). In those studies, BCC lesion discrimination was focused on the detection and analysis of particular dermoscopic features, including telangiectasia (3,4), leaf-dirt trails (5), and semitranslucency (6).…”

“…While there have been numerous studies based on dermoscopic image feature analysis for pigmented lesion discrimination, few studies have specifically addressed BCC versus benign lesion discrimination by a classifier . In those studies, BCC lesion discrimination was focused on the detection and analysis of particular dermoscopic features, including telangiectasia , leaf‐dirt trails , and semitranslucency . This research explores the efficacy of fusing clinical and dermoscopic features to enhance skin lesion discrimination capability.…”

Analytic Dermoscopy of Superficial Basal Cell Carcinoma

“…Some other attempts at objective assessments of BCC dermoscopic features were initiated in a recent past [6,7]. The present study focused on some of the most typical dermoscopic BCC features, namely the spoke-wheel areas.…”

Artificial Intelligence Applications in Dermatology: Where Do We Stand?

“…An increasing number of original studies have also begun classifying non-melanoma skin cancers (also known as keratinocyte carcinomas) vs. benign and pre-malignant lesions (36)(37)(38)(39)(40)(41)(42)(43)(44). For example, Spyridonos et al developed an AI model that could differentiate between actinic keratosis and normal skin with a specificity of 89.8% and a sensitivity of 91.7% (37).…”