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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 resourceconstrained 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. Our extensive evaluation on an image dataset provided by National Skin Center -Singapore (117 benign nevi and 67 malignant melanoma) confirms the effectiveness of the proposed system for melanoma detection: 89.09% sensitivity at specificity ≥ 90%.

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Abstract. We present an algorithm for fragile watermarking of color, or multi-channel, images either in uncompressed format, in lossless compressed format, or in compressed format with locally compressed units (like JPEG). The watermark is embedded into the Karhunen-Loève transform (KLT) coefficients of the host image, and the security of the method is based on the secrecy of the KLT basis computed from a corresponding secret key image. The watermark bits may be embedded with various methods, in particular the use of syndrome coding has proven flexible and effective.Genetic Algorithms (GAs) are used to find the optimum pixel modification leading to the watermark embedding. The resulting watermarked images have shown to have both a high objective quality (in terms of PSNR and SSIM) and a high subjective quality (tested by a group of observers). At the same time, the watermark extraction is very sensitive to small (±1 intensity level for single pixel) modifications, ensuring image authentication with very high probability.

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