We demonstrate an automatic recognition strategy for terahertz (THz) pulsed signals of breast invasive ductal carcinoma (IDC) based on a wavelet entropy feature extraction and a machine learning classifier. The wavelet packet transform was implemented into the complexity analysis of the transmission THz signal from a breast tissue sample. A novel index of energy to Shannon entropy ratio (ESER) was proposed to distinguish different tissues. Furthermore, the principal component analysis (PCA) method and machine learning classifier were further adopted and optimized for automatic classification of the THz signal from breast IDC sample. The areas under the receiver operating characteristic curves are all larger than 0.89 for the three adopted classifiers. The best breast IDC recognition performance is with the precision, sensitivity and specificity of 92.85%, 89.66% and 96.67%, respectively. The results demonstrate the effectiveness of the ESER index together with the machine learning classifier for automatically identifying different breast tissues.

We demonstrate Fourier single-pixel imaging in the terahertz regime. The experimental system is implemented with a photo-induced coded aperture setup, where a monolayer graphene on a high-resistance silicon substrate illuminated by a coded laser beam works as a terahertz modulator. Results show that high-quality terahertz images can be reconstructed using greatly reduced number of measurements. We further find that deep photo-induced terahertz modulation by adding a monolayer graphene on the silicon substrate and by using high laser power can significantly improve the image quality. Compared to Hadamard single-pixel imaging with re-ordered Hadamard matrix, the Fourier approach has higher image quality. We expect that this work will speed up the efficiency of single-pixel terahertz imaging and advance terahertz imaging applications.