Abstract:
Improving resolution and removing speckle noise from medical ultrasound images while preserving tissue texture, small details, and edges without introducing artifact and distortion is a major challenge in ultrasound image restoration. The underlying physical phenomena related with US image acquisition and imperfection of US imaging system design give rise to low resolution and speckle noise that tend to reduce the image con- trast, obscure and blur image details such as inclusion and small structure boundary, tissue texture and thereby, decrease the quality and reliability of medical ultrasound. In this thesis, a complete framework of signal processing approaches comprising of de- convolution to enhance resolution, despeckling, and post-processing for the generation of ultrasound B-mode image with superior edge, details and tissue texture has been established. In the first step, we propose a correlation constrained blind multichannel frequency-domain least-mean-squares (bMCFLMS) algorithm to undo the effect of point spread function (PSF) on the ultrasound radio-frequency (RF) data. The bMCFLMS algorithm, however, shows misconvergence due to both channel noise and propagation of TRF estimation error from the previous blocks. This phenomenon is more intense in the case of md-bMCFLMS algorithm because of increased estimation error. To address this problem, a novel constraint based on the correlation between the measured RF data and estimated TRF is proposed in this thesis. Then in the second step, based on a multiple input single output (MISO) model over the consecutive de- convolved ultrasound image frames, a multiframe-based adaptive despeckling (MADS) algorithm to reconstruct a high-resolution B-mode image from raw radio-frequency (RF) data has been proposed. It utilizes the speckle patterns estimated using a novel multiframe-based adaptive approach for ultrasonic speckle noise estimation (MSNE) based on a single input multiple output (SIMO) modeling of consecutive deconvolved ultrasound image frames to estimate the despeckled ultrasound image as single output from the deconvolved image frames as multiple input. The elegance of the proposed al- gorithms is that it addresses the deconvolution and despeckling problem by completely following the signal generation model rather than the existing ad-hoc smoothening or filtering approach described in the literature, and therefore, it is likely to maximally preserve the image features. The efficacy of our proposed blind deconvolution algorithm is measured using simulation phantom and in-vivo data. The proposed md-bMCFLMS algorithm shows normalised projection misalignment (NPM) improvement of about 2.12 ⇠ 16 dB and resolution gain (RG) improvement of 1.14 ⇠ 6.4 dB compared to other techniques in the literature. Moreover, because of the frequency-domain implementation it is computationally more efficient, fast converging and robust than its time-domain counterpart l1-bMCLMS algorithm reported in the literature. Again, the efficacy of the proposed despeckling algorithm is evaluated both visually and quantitatively on the simulation and in-vivo data. The results show 8.5515.91 dB, 8.2414.94 dB, 0.57 7.03 improvement in terms of SNR, PSNR, and NIQE, respectively, for simulation data and 2.22 3.17 improvement in terms of NIQE for in-vivo data compared to the traditional despeckling algorithms.
