Analysis and modeling techniques for ultrasonic tissue characterization

This thesis introduces new processing techniques for computer-aided interpretation of ultrasound images with the purpose of supporting medical diagnostic. In terms of practical application, the goal of this work is the improvement of current prostate biopsy protocols by providing physicians with a visual map overlaid over ultrasound images marking regions potentially affected by disease. As far as analysis techniques are concerned, the main contributions of this work to the state-of-the-art is the introduction of deconvolution as a pre-processing step in the standard ultrasonic tissue characterization procedure to improve the diagnostic significance of ultrasonic features. This thesis also includes some innovations in ultrasound modeling, in particular the employment of a continuous-time autoregressive moving-average (CARMA) model for ultrasound signals, a new maximum-likelihood CARMA estimator based on exponential splines and the definition of CARMA parameters as new ultrasonic features able to capture scatterers concentration. Finally, concerning the clinical usefulness of the developed techniques, the main contribution of this research is showing, through a study based on medical ground truth, that a reduction in the number of sampled cores in standard prostate biopsy is possible, preserving the same diagnostic power of the current clinical protocol.

Small Area Estimation of Economic Security

The objective of this thesis is the small area estimation of an economic security indicator. Economic security is a complex concept that carries a variety of meanings. In the literature there is no a formal unambiguous definition for economic security and in this work we refer to the definition recently provided for its opposite, economic insecurity, as the “anxiety produced by the possible exposure to adverse economic events and by the anticipation of the difficulty to recover from them” (Bossert and D’Ambrosio, 2013). In the last decade interest for economic insecurity/security has grown constantly, especially since the financial crisis of 2008, but even more in the last year after the economic consequences due to the Covid-19 pandemic. In this research, economic security is measures through a longitudinal indicator that takes into account the income levels of Italian households, from 2014 to 2016. The target areas are groups of Italian provinces, for which the indicator is estimated using longitudinal data taken from EU-SILC survey. We notice that the sample size is too low to obtain reliable estimates for our target areas. Therefore we resort to some Small Area Estimation strategies to improve the reliability of the results. In particular we consider small area models specified at area level. Besides the basic Fay-Herriot area-level model, we propose to consider some longitudinal extensions, including time-specific random effects following an autoregressive processes of order 1 (AR1) and a moving average of order 1 (MA1). We found that all the small area models used show a significant efficiency gain, especially MA1 model.