2008/09/20 by M. Emre Celebi, Hitoshi Iyatomi, William V. Stoecker +5 · 2 citations
Computer Science · Medicine · #Artificial intelligence #Biology #Computer graphics (images) #Computer science #Computer vision #Cutaneous Melanoma Detection and Management #Dermatologic Treatments and Research #Nonmelanoma Skin Cancer Studies #Pattern recognition (psychology) #White (mutation) #cs.CV
paper · pdf · doi:10.1016/j.compmedimag.2008.08.003
published as Computerized Medical Imaging and Graphics 32 (2008) 670-677
openalex publication_date 2008/09/20 · arxiv created 2010/09/06 · arxiv updated 2011/11/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Dermoscopy is a non-invasive skin imaging technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One of the most important features for the diagnosis of melanoma in dermoscopy images is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white "ground-glass" film). In this article, we present a machine learning approach to the detection of blue-white veil and related structures in dermoscopy images. The method involves contextual pixel classification using a decision tree classifier. The percentage of blue-white areas detected in a lesion combined with a simple shape descriptor yielded a sensitivity of 69.35% and a specificity of 89.97% on a set of 545 dermoscopy images. The sensitivity rises to 78.20% for detection of blue veil in those cases where it is a primary feature for melanoma recognition.