  We investigate the design of an entire mobile imaging system for early
detection of melanoma. Different from previous work, we focus on
smartphone-captured visible light images. Our design addresses two major
challenges. First, images acquired using a smartphone under loosely-controlled
environmental conditions may be subject to various distortions, and this makes
melanoma detection more difficult. Second, processing performed on a smartphone
is subject to stringent computation and memory constraints. In our work, we
propose a detection system that is optimized to run entirely on the
resource-constrained smartphone. Our system intends to localize the skin lesion
by combining a lightweight method for skin detection with a hierarchical
segmentation approach using two fast segmentation methods. Moreover, we study
an extensive set of image features and propose new numerical features to
characterize a skin lesion. Furthermore, we propose an improved feature
selection algorithm to determine a small set of discriminative features used by
the final lightweight system. In addition, we study the human-computer
interface (HCI) design to understand the usability and acceptance issues of the
proposed system.
