The segmentation of cancer-suspicious skin lesions using ultrasound may help their differential diagnosis and treatment planning. Active contour models (ACM) require an initial seed, which when manually chosen may cause variations in segmentation accuracy. Fully-automated skin segmentation typically employs layer-by-layer segmentation using a combination of methods; however, such segmentation has not yet been applied on cancerous lesions. In the current work, fully automated segmentation is achieved in two steps: an automated seeding (AS) step using a layer-by-layer method followed by a growing step using an ACM. The method was tested on images of nevi, melanomas, and basal cell carcinomas from two ultrasound imaging systems (N=60), with all lesions being successfully located. For the seeding step, manual seeding (MS) was used as a reference. AS approached the accuracy of MS when the latter used an optimal bounding rectangle based on the ground truth (Sørensen-Dice coefficient (SDC) of 72.3 vs 74.6, respectively). The effect of varying the manual seed was also investigated; a 0.7 decrease in seed height and width caused a mean SDC of 54.6. The results show the robustness of automated seeding for skin lesion segmentation.
Keywords: Automated layer segmentation; Automated lesion localization; Computer vision; Seed selection; Skin ultrasound.
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