Observed medical ultrasound images are degraded representations of true tissue images. The degradation is a combination of blurring due to the finite resolution of the imaging system and the observation noise. This paper presents a new wavelet based deconvolution method for medical ultrasound imaging. We design a new orthogonal wavelet basis known as the <i>symmetrical mirror wavelet basis</i> that can provide more desirable frequency resolution. Our proposed ultrasound image restoration with wavelets consists of an inversion of the observed ultrasound image using the estimated two-dimensional (2-D) point spread function (PSF) followed by denoising in the designed wavelet basis. The tissue image restoration is then accomplished by modelling the tissue structures with the generalized Gaussian density (GGD) function using the Bayesian estimation. Both subjective and objective measures show that the deconvolved images are more appealing in the visualization and resolution gain.
In this paper, a Feldkamp-type approximate algorithm is proposed for helical multislice Computed Tomography (CT) image reconstruction. For a planar transversal reconstruction slice under consideration, the algorithm adopts a set of scanning data samples such that all points of the planar plane satisfy Tuy's exact reconstruction condition and, therefore, have potential to be exactly reconstructed. This can provide a practically feasible compromise between image quality and computation efficiency in the reconstruction. Simulation results can show advantages of this algorithm in reduction of artifacts and improvement of computational efficiency in comparison with the existing algorithms.
FDK algorithm has been known to be a popular 3D approximate computed tomography (CT) reconstruction algorithm. However, it may not provide satisfactory image quality for large cone angle. Recently, it has been improved by performing ramp filtering along the direction tangent to the helix, so to provide improved image quality for large cone angle. In this paper, we present a FDK type approximate reconstruction algorithm for gantry-tilted CT imaging. The proposed method improves FDK algorithm by filtering the projection data along a proper direction. Its filtering direction is determined by CT parameters and gantry-tilted angle. As a result, the proposed gantry-tilted reconstruction algorithm can provide more scanning flexibilities in clinical CT scanning and is efficient in computation. The performance of the proposed algorithm is evaluated with Turbell Clock phantom and Thorax phantom compared with gantry tilted FDK algorithm and a popular 2D approximate algorithm. The results show that our new algorithm can achieve better image quality than FDK algorithm and the 2D approximate algorithm for gantry-tilted CT image reconstruction.
In order to research the global change and analyze the characteristics of land surface or atmosphere, an Earth Observation Station usually needs a high-definition large screen to display the satellite remote sensing image. Under the development of digital imaging technology, this application can be put into practice. In this paper, the optical engine, a key technique in the digital imaging, is introduced. Then the basic principle and technical difficulties of optical engine are discussed in detail. It is testified that the optical engine technique can make the satellite remote sensing image displayed in high-definition model.
In this paper the authors analyzed the data collection of seabed terrain, the influence factors on measurement precision and the data computation in time or frequency domain. In order to estimate the noise embedded in the received data, the structure and algorithm of characteristic matching filter based on the entropy concept is developed and discussed in detail. It is shown that the noise data can be removed effectively and the high precision seabed terrain be simulated by the filtering method.