Automatic segmentation methodology for dermatological images acquired via mobile devices

Nowadays, skin cancer is considered one of the most common malignancies in the Caucasian population, thus it is crucial to develop methodologies to prevent it. Because of that, Mobile Teledermatology (MT) is thriving, allowing patients to adopt an active role in their health status while facilitating doctors to early diagnose skin cancers. Skin lesion segmentation is one of the most important and difficult task in computerized image analysis process, and so far the attention is mainly turned to dermoscopic images. In order to turn MT more accurate, it is therefore fundamental to develop simple segmentation methodologies specifically designed for macroscopic images or images acquired via smartphones, which is the main focus of this work. The proposed method was applied in 80 images acquired via smartphones and promising results have been achieved: a mean Jaccard index of 81%, mean True Detection Rate of 96% and mean Accuracy around 98%. The major goal of this work is to develop a mobile application easily accessible for the general population, with the aim of raise awareness and help both patients and doctors in the early diagnosis of skin cancers.