An effective method to develop anatomically real numerical breast phantoms for T1-weighted MR images of different tissue densities is presented. The dielectric properties for breast tissues are calculated and analyzed using different dispersion models (i.e., one- and two-pole cole–cole and Debye models). The method presented in this paper propounds significant improvements in comparison with existing MRI-based numerical phantoms in terms of denoising of images, tissue segmentation, nonlinear mapping of dielectric properties with realistic shapes using all the dispersion models and densitywise classification of phantoms. This method is a multistep approach in which each MRI voxel is mapped with the appropriate dielectric properties according to different dispersion models. The MRI data was collected and interpolated according to the size of the uniform grid for finite difference time domain computations followed by the preprocessing of MR images to enhance them. Thereafter, the voxel intensities were segregated into two groups as adipose and fibro glandular tissues. These tissue intensities were assigned the corresponding dielectric properties. Three-dimensional (3-D) numerical phantoms were created according to all the dispersion models. After the comparison among the models, it has been found that, along with frequency, the dielectric properties vary according to the variation of the dispersion model parameters. It was also observed that the dielectric properties calculated from one-pole cole–cole and two-pole Debye models are more close to the real properties of breast tissues than other models. A generalized method has been defined for developing the 3-D phantoms for all classes of breast according to the inhomogeneity of fibroglandular tissues using the dispersion models. The frequency-dependent and dispersion model parameters-dependent dielectric properties have been assigned to the phantoms. These real-like phantoms after 3-D printing would help researchers working in the field of breast cancer detection studies.
The presence of speckle noise in the reconstruction process of digital hologram reduces the signal to noise ratio (SNR) in
the reconstructed images. In this paper we present wavelet filtering to improve SNR in the reconstructed images.
Experimental results are presented.
A swept source system was realized in the wavelength range of 810-875 nm with the combination of a broad-band superluminescent diode (SLD) and an acousto-optic tunable filter (AOTF) as a frequency-tuning device. SLD has two spectral centers at 820 nm and 845 nm with spectral bandwidth (FWHM) of around 40 nm. Gaussian spectral shaping
was performed onto the original SLD spectrum while reconstructing OCT images for various test samples such as onion
slice and fingerprint impression taken on a glass substrate. As a pulse can be considered a Gaussian distribution of
frequencies, spectral shaping yields sharper Fourier peaks. Application of Gaussian spectrum facilitates in precisely
locating the reflective boundaries within the sample that results in improved OCT images.
A method to measure the temperature by visualizing the laminar free convection flow of water is presented by using
digital holography in lensless Fourier transform configuration. Temperature is measured within the boundary layer of the
convective flow field. The deviation in temperature measured by this method and from that of obtained by thermocouple
is within 2%.