Hyperspectral Imaging (HSI) is a promising practice in research medicine due to its non-contact, non-invasive, non-ionizing, and label-free characteristics. Chromophores, such as haemoglobin and melanin, are responsible for the chemical structure of tissues and determine their spectral properties. Therefore, hyperspectral technologies might serve the role of tissue diagnosis, aiding physicians during surgical or clinical operations. Hence, hyperspectral cameras produce the data used by machine and deep learning algorithms to discriminate healthy from damaged tissues. Nevertheless, data quality remains an issue, especially concerning the small-sized medical dataset available to research. Here, we propose a hyperspectral imaging blueprint, designed to work with push broom sensors, representing one of the highest quality transducers to acquire spectral data. Indeed, push broom sensors only seize one scene line at a time, offering high spatial and spectral resolutions. It can work in any scenario, such as dermatological or surgical, involving a motionless subject. We designed the system to be affordable, open-source and robust. Therefore, it comprises Python libraries, an Arduino one board, a Nema17 stepper motor, its driver controller, and a recirculating ball screw for accurate movement. Furthermore, it offers a diode-based targeting system, attached to a 3D printed circular crown and built to hit the image capture and measure the right focusing distance. We equipped the blueprint with a graphical user interface to let physicians interact with the camera, accurately move it, and acquire the diagnostic data needed.
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