Pattern-based segmentation of digital documents: model and implementation

This thesis proposes a new document model, according to which any document can be segmented in some independent components and transformed in a pattern-based projection, that only uses a very small set of objects and composition rules. The point is that such a normalized document expresses the same fundamental information of the original one, in a simple, clear and unambiguous way. The central part of my work consists of discussing that model, investigating how a digital document can be segmented, and how a segmented version can be used to implement advanced tools of conversion. I present seven patterns which are versatile enough to capture the most relevant documents’ structures, and whose minimality and rigour make that implementation possible. The abstract model is then instantiated into an actual markup language, called IML. IML is a general and extensible language, which basically adopts an XHTML syntax, able to capture a posteriori the only content of a digital document. It is compared with other languages and proposals, in order to clarify its role and objectives. Finally, I present some systems built upon these ideas. These applications are evaluated in terms of users’ advantages, workflow improvements and impact over the overall quality of the output. In particular, they cover heterogeneous content management processes: from web editing to collaboration (IsaWiki and WikiFactory), from e-learning (IsaLearning) to professional printing (IsaPress).

A skin surface characterization system based on capacitive image analysis

During the last few years, several methods have been proposed in order to study and to evaluate characteristic properties of the human skin by using non-invasive approaches. Mostly, these methods cover aspects related to either dermatology, to analyze skin physiology and to evaluate the effectiveness of medical treatments in skin diseases, or dermocosmetics and cosmetic science to evaluate, for example, the effectiveness of anti-aging treatments. To these purposes a routine approach must be followed. Although very accurate and high resolution measurements can be achieved by using conventional methods, such as optical or mechanical profilometry for example, their use is quite limited primarily to the high cost of the instrumentation required, which in turn is usually cumbersome, highlighting some of the limitations for a routine based analysis. This thesis aims to investigate the feasibility of a noninvasive skin characterization system based on the analysis of capacitive images of the skin surface. The system relies on a CMOS portable capacitive device which gives 50 micron/pixel resolution capacitance map of the skin micro-relief. In order to extract characteristic features of the skin topography, image analysis techniques, such as watershed segmentation and wavelet analysis, have been used to detect the main structures of interest: wrinkles and plateau of the typical micro-relief pattern. In order to validate the method, the features extracted from a dataset of skin capacitive images acquired during dermatological examinations of a healthy group of volunteers have been compared with the age of the subjects involved, showing good correlation with the skin ageing effect. Detailed analysis of the output of the capacitive sensor compared with optical profilometry of silicone replica of the same skin area has revealed potentiality and some limitations of this technology. Also, applications to follow-up studies, as needed to objectively evaluate the effectiveness of treatments in a routine manner, are discussed.

Influence of processing conditions on quality aspects of potato-based fresh pasta

Gnocchi is a typical Italian potato-based fresh pasta that can be either homemade or industrially manufactured. The homemade traditional product is consumed fresh on the day it is produced, whereas the industrially manufactured one is vacuum-packed in polyethylene and usually stored at refrigerated conditions. At industrial level, most kinds of gnocchi are usually produced by using some potato derivatives (i.e. flakes, dehydrated products or flour) to which soft wheat flour, salt, some emulsifiers and aromas are added. Recently, a novel type of gnocchi emerged on the Italian pasta market, since it would be as much similar as possible to the traditional homemade one. It is industrially produced from fresh potatoes as main ingredient and soft wheat flour, pasteurized liquid eggs and salt, moreover this product undergoes steam cooking and mashing industrial treatments. Neither preservatives nor emulsifiers are included in the recipe. The main aim of this work was to get inside the industrial manufacture of gnocchi, in order to improve the quality characteristics of the final product, by the study of the main steps of the production, starting from the raw and steam cooked tubers, through the semi-finished materials, such as the potato puree and the formulated dough. For this purpose the investigation of the enzymatic activity of the raw and steam cooked potatoes, the main characteristics of the puree (colour, texture and starch), the interaction among ingredients of differently formulated doughs and the basic quality aspects of the final product have been performed. Results obtained in this work indicated that steam cooking influenced the analysed enzymes (Pectin methylesterase and α- and β-amylases) in different tissues of the tuber. PME resulted still active in the cortex, it therefore may affect the texture of cooked potatoes to be used as main ingredient in the production of gnocchi. Starch degrading enzymes (α- and β-amylases) were inactivated both in the cortex and in the pith of the tuber. The study performed on the potato puree showed that, between the two analysed samples, the product which employed dual lower pressure treatments seemed to be the most suitable to the production of gnocchi, in terms of its better physicochemical and textural properties. It did not evidence aggregation phenomena responsible of hard lumps, which may occur in this kind of semi-finished product. The textural properties of gnocchi doughs were not influenced by the different formulation as expected. Among the ingredients involved in the preparation of the different samples, soft wheat flour seemed to be the most crucial in affecting the quality features of gnocchi doughs. As a consequence of the interactive effect of the ingredients on the physicochemical and textural characteristics of the different doughs, a uniform and well-defined split-up among samples was not obtained. In the comparison of different kinds of gnocchi, the optimal physicochemical and textural properties were detected in the sample made with fresh tubers. This was probably caused not only by the use of fresh steam cooked potatoes, but also by the pasteurized liquid eggs and by the absence of any kind of emulsifier, additive or preserving substance.

Automated Segmentation and Non-Rigid Registration for the Quantitative Evaluation of Myocardial Perfusion from Magnetic Resonance Images

Myocardial perfusion quantification by means of Contrast-Enhanced Cardiac Magnetic Resonance images relies on time consuming frame-by-frame manual tracing of regions of interest. In this Thesis, a novel automated technique for myocardial segmentation and non-rigid registration as a basis for perfusion quantification is presented. The proposed technique is based on three steps: reference frame selection, myocardial segmentation and non-rigid registration. In the first step, the reference frame in which both endo- and epicardial segmentation will be performed is chosen. Endocardial segmentation is achieved by means of a statistical region-based level-set technique followed by a curvature-based regularization motion. Epicardial segmentation is achieved by means of an edge-based level-set technique followed again by a regularization motion. To take into account the changes in position, size and shape of myocardium throughout the sequence due to out of plane respiratory motion, a non-rigid registration algorithm is required. The proposed non-rigid registration scheme consists in a novel multiscale extension of the normalized cross-correlation algorithm in combination with level-set methods. The myocardium is then divided into standard segments. Contrast enhancement curves are computed measuring the mean pixel intensity of each segment over time, and perfusion indices are extracted from each curve. The overall approach has been tested on synthetic and real datasets. For validation purposes, the sequences have been manually traced by an experienced interpreter, and contrast enhancement curves as well as perfusion indices have been computed. Comparisons between automatically extracted and manually obtained contours and enhancement curves showed high inter-technique agreement. Comparisons of perfusion indices computed using both approaches against quantitative coronary angiography and visual interpretation demonstrated that the two technique have similar diagnostic accuracy. In conclusion, the proposed technique allows fast, automated and accurate measurement of intra-myocardial contrast dynamics, and may thus address the strong clinical need for quantitative evaluation of myocardial perfusion.

Lesion detection and classification in breast cancer: evaluation of approaches based on morphological features, tracer kinetic modelling and semi-quantitative parameters in MR functional imaging (DCE-MRI)

The diagnosis, grading and classification of tumours has benefited considerably from the development of DCE-MRI which is now essential to the adequate clinical management of many tumour types due to its capability in detecting active angiogenesis. Several strategies have been proposed for DCE-MRI evaluation. Visual inspection of contrast agent concentration curves vs time is a very simple yet operator dependent procedure, therefore more objective approaches have been developed in order to facilitate comparison between studies. In so called model free approaches, descriptive or heuristic information extracted from time series raw data have been used for tissue classification. The main issue concerning these schemes is that they have not a direct interpretation in terms of physiological properties of the tissues. On the other hand, model based investigations typically involve compartmental tracer kinetic modelling and pixel-by-pixel estimation of kinetic parameters via non-linear regression applied on region of interests opportunely selected by the physician. This approach has the advantage to provide parameters directly related to the pathophysiological properties of the tissue such as vessel permeability, local regional blood flow, extraction fraction, concentration gradient between plasma and extravascular-extracellular space. Anyway, nonlinear modelling is computational demanding and the accuracy of the estimates can be affected by the signal-to-noise ratio and by the initial solutions. The principal aim of this thesis is investigate the use of semi-quantitative and quantitative parameters for segmentation and classification of breast lesion. The objectives can be subdivided as follow: describe the principal techniques to evaluate time intensity curve in DCE-MRI with focus on kinetic model proposed in literature; to evaluate the influence in parametrization choice for a classic bi-compartmental kinetic models; to evaluate the performance of a method for simultaneous tracer kinetic modelling and pixel classification; to evaluate performance of machine learning techniques training for segmentation and classification of breast lesion.