Short optical pulses emitted from a tunable Q-switched laser (800 to 2000 nm) generate laser ultrasound (LUS) signals at the surface of biological tissue. The LUS signal’s acoustic frequency content, dependence on sample type, and optical wavelength are observed in the far field. The experiments yield a reference dataset for the design of noncontact LUS imaging systems. Measurements show that the majority of LUS signal energy in biological tissues is within the 0.5 and 3 MHz frequency bands and the total acoustic energy generated increases with the optical absorption coefficient of water, which governs tissue optical absorption in the infrared range. The experimental results also link tissue surface roughness and acoustic attenuation with limited LUS signal bandwidth in biological tissue. Images constructed using 810-, 1064-, 1550-, and 2000-nm generation laser wavelengths and a contact piezoelectric receiver demonstrates the impact of the generation laser wavelength on image quality. A noncontact LUS-based medical imaging system has the potential to be an effective medical imaging device. Such a system may mitigate interoperator variability associated with current medical ultrasound imaging techniques and expand the scope of imaging applications for ultrasound.
An important aspect of hyperspectral pattern recognition is selecting a subset of bands to perform the classification. This is generally necessary because the statistical algorithms on which classification is based need probabilistic estimates to work. The great number of spectral bands in hyperspectral images means that there is not enough data to accurately perform these estimates. In typical hyperspectral pattern recognition, the band selection and classification stages are done separately. This paper presents research done with an iterative system that integrates the band selection and classification. The objective is to choose an optimal subgroup of bands by maximizing the distance between the centroids of the classified data. The results of the study show that: (1) the algorithm correctly chooses the best bands based on centroid separability with synthetic data, (2) the system converges, and (3) the percentage of samples classified correctly using the iterative system is greater than the percentage using all the bands.