While RGB imaging is reaching its limits, Hyperspectral Imaging (HSI) is being widely used especially for medical applications. This study points out the ability of HSI technique to help in planning the surgical procedure in orthopedic surgery by automatically identifying anatomical structures and surgical instruments thanks to their spectral signatures. Four segmentation methods have been explored: (i) average spectra method that uses the Euclidean distance between the spectrum of each pixel and the average spectrum of each specific structure, (ii) segmentation using kmeans, (iii) segmentation based on indices in which we identify reflectances ratios at specific wavelengths that allow materials to be correctly classified, (iv) and finally a pixel-based classification method based on neural networks. Experiments on anatomical objects whose physical characteristics are known to have been carried out. Selecting specific wavelengths to reduce the cost of the final device was also discussed.
Polarization-resolved extension of Second Harmonic Generation microscopy (PSHG) exhibits proven efficiency in cancer diagnosis. Contrary to the case of white light microscopy, PSHG can reveal small structural collagen changes, during tumorigenesis, for a broad range of organs such as breast, thyroid, lung, pancreas, and ovary. However, despite its effectiveness for cancer diagnosis, PSHG is not yet fully exploited. One way of improvement consists in taking better advantage of polarization-resolved measurements which are performed by acquiring multiple images (usually between three to 20) of the same sample under different input beam polarization conditions. Each image of the resulting stacked raw images set can contain relevant information not found in the other images of the set. In the literature, information extraction from stacked raw images is performed using methods such as averaging of all images, collagen structural parameters modeling or PSHG polarimetric parameters extraction. If the two latter methods provide a richer information than the first one, they may, however, suffer from a loss of information from the stacked raw images. To examine this potential loss of information, AI methods can be used for extracting information from the stacked raw images. Using recently available images of the public SHG-TIFF database, dealing with breast and thyroid PSHG measurements of both normal and tumor tissues, we test available AI methods for information extraction and benchmark these methods to the state-of-the-art, in terms of automatic cancer diagnosis efficiency.
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