|Ph.D Student||Michailovich Oleg|
|Subject||A Novel Approach to 2-D Blind Deconvolution of Ultrasound|
Images Using Projections into Complex Compactly
Supported Orthonormal Bases
|Department||Department of Biomedical Engineering||Supervisor||Professor Emeritus Dan Adam|
Medical imaging is the scientific branch concerned with the reproduction of anatomical structure and physiological function of the human body. Today, several imaging modalities are available as standard equipment, among which medical ultrasound imaging is currently considered to be non-invasive, practically harmless, accurate and cost-effective modality for investigating the biological tissues. Unfortunately, the above advantages of medical ultrasound imaging are not “gratuitous” and the price paid for them is a considerably reduced image quality (e.g. reduced resolution and contrast, increased noise levels). It has resulted in intensive development of algorithms for the restoration of ultrasound images, among which are the blind-deconvolution techniques. Within the framework of these methods, a typical ultrasound image is considered to be a distorted version of an original image (commonly referred to as tissue reflectivity function), where the distortion operator is the convolution with the point spread function (PSF) of the imaging system. Since the PSF may change considerably due to the presence of the interrogated tissue, its estimation can be viewed as a preliminary stage of the problem of the restoration of ultrasound images.
The contribution of the present thesis is threefold. First, it defines a general framework for the blind deconvolution approaches, generalizing the basic concepts of homomorphic signal processing. Second, a number of novel methods are proposed in order to recover the PSF. These methods include linear and non-linear approaches, and also methods based on either statistical or analytic properties of the signals to be estimated. Third, the study shows that given a reliable estimate of the PSF, it is possible to deconvolve it out of the RF-image in order to obtain an estimate of the true reflectivity function, which is relatively independent of the imaging system properties.