Abstract

We present an instrument based on commodity embedded hardware, that implements an automatic procedure for early skin-cancer screening using dynamic thermal imaging. The procedure leverages image segmentation in the visible range and real-time multimodal registration to compute the temperature recovery curve (TRC) of suspicious skin lesions using thermal infrared video. The instrument implements two algorithms that infer the malignancy of the lesion from the computed TRCs. The first algorithm assumes that the TRCs are deterministic and infers the malignancy from the distance between the TRC of the suspicious lesion and its surrounding skin, which is assumed to be healthy tissue. The second algorithm models the TRC of the lesion as a random process and uses detection theory to statistically infer its malignancy from the eigenfunctions and corresponding eigenvalues of its covariance function. We built a prototype of the instrument using a Raspberry Pi 3 model B+ board, which acquires a visible-range image of the lesion at the beginning of the procedure and performs image segmentation in 62ms. Operating on a 400 x 400-pixel region-of-interest within the infrared video, the board performs frame-by-frame multimodal image registration and generates the TRCs in real time at more than 37 frames per second, thus eliminating the need to store video data for off-line processing. The statistical detection algorithm, which yields the best results, runs in 1.07s at the end of the procedure, and achieves a sensitivity of 98% and a specificity of 95% on a dataset of 116 volunteer subjects.