Statistical methods for biomedical signal analysis and processing

Statistical modelling and statistical learning theory are two powerful analytical frameworks for analyzing signals and developing efficient processing and classification algorithms. In this thesis, these frameworks are applied for modelling and processing biomedical signals in two different contexts: ultrasound medical imaging systems and primate neural activity analysis and modelling. In the context of ultrasound medical imaging, two main applications are explored: deconvolution of signals measured from a ultrasonic transducer and automatic image segmentation and classification of prostate ultrasound scans. In the former application a stochastic model of the radio frequency signal measured from a ultrasonic transducer is derived. This model is then employed for developing in a statistical framework a regularized deconvolution procedure, for enhancing signal resolution. In the latter application, different statistical models are used to characterize images of prostate tissues, extracting different features. These features are then uses to segment the images in region of interests by means of an automatic procedure based on a statistical model of the extracted features. Finally, machine learning techniques are used for automatic classification of the different region of interests. In the context of neural activity signals, an example of bio-inspired dynamical network was developed to help in studies of motor-related processes in the brain of primate monkeys. The presented model aims to mimic the abstract functionality of a cell population in 7a parietal region of primate monkeys, during the execution of learned behavioural tasks.

Statistical methods for analysis and processing of medical ultrasound: applications to segmentation and restoration

In this thesis two major topics inherent with medical ultrasound images are addressed: deconvolution and segmentation. In the first case a deconvolution algorithm is described allowing statistically consistent maximum a posteriori estimates of the tissue reflectivity to be restored. These estimates are proven to provide a reliable source of information for achieving an accurate characterization of biological tissues through the ultrasound echo. The second topic involves the definition of a semi automatic algorithm for myocardium segmentation in 2D echocardiographic images. The results show that the proposed method can reduce inter- and intra observer variability in myocardial contours delineation and is feasible and accurate even on clinical data.

Analysis of human body kinematics using a hybrid markerless video acquisition and processing

A main objective of the human movement analysis is the quantitative description of joint kinematics and kinetics. This information may have great possibility to address clinical problems both in orthopaedics and motor rehabilitation. Previous studies have shown that the assessment of kinematics and kinetics from stereophotogrammetric data necessitates a setup phase, special equipment and expertise to operate. Besides, this procedure may cause feeling of uneasiness on the subjects and may hinder with their walking. The general aim of this thesis is the implementation and evaluation of new 2D markerless techniques, in order to contribute to the development of an alternative technique to the traditional stereophotogrammetric techniques. At first, the focus of the study has been the estimation of the ankle-foot complex kinematics during stance phase of the gait. Two particular cases were considered: subjects barefoot and subjects wearing ankle socks. The use of socks was investigated in view of the development of the hybrid method proposed in this work. Different algorithms were analyzed, evaluated and implemented in order to have a 2D markerless solution to estimate the kinematics for both cases. The validation of the proposed technique was done with a traditional stereophotogrammetric system. The implementation of the technique leads towards an easy to configure (and more comfortable for the subject) alternative to the traditional stereophotogrammetric system. Then, the abovementioned technique has been improved so that the measurement of knee flexion/extension could be done with a 2D markerless technique. The main changes on the implementation were on occlusion handling and background segmentation. With the additional constraints, the proposed technique was applied to the estimation of knee flexion/extension and compared with a traditional stereophotogrammetric system. Results showed that the knee flexion/extension estimation from traditional stereophotogrammetric system and the proposed markerless system were highly comparable, making the latter a potential alternative for clinical use. A contribution has also been given in the estimation of lower limb kinematics of the children with cerebral palsy (CP). For this purpose, a hybrid technique, which uses high-cut underwear and ankle socks as “segmental markers” in combination with a markerless methodology, was proposed. The proposed hybrid technique is different than the abovementioned markerless technique in terms of the algorithm chosen. Results showed that the proposed hybrid technique can become a simple and low-cost alternative to the traditional stereophotogrammetric systems.

Farming and processing stategies for improving poultry meat quality

The purpose of the PhD research was the identification of new strategies of farming and processing, with the aim to improve the nutritional and technological characteristics of poultry meat. Part of the PhD research was focused on evaluation of alternative farming systems, with the aim to increase animal welfare and to improve the meat quality and sensorial characteristics in broiler chickens. It was also assessed the use of innovative ingredients for marination of poultry meat (sodium bicarbonate and natural antioxidants) The research was developed by studying the following aspects: - Meat quality characteristics, oxidative stability and sensorial traits of chicken meat obtained from two different farming systems: free range vs conventional; - Meat quality traits of frozen chicken breast pre-salted using increasing concentrations of sodium chloride; - Use of sodium bicarbonate in comparison with sodium trypolyphosphate for marination of broiler breast meat and phase; - Marination with thyme and orange essential oils mixture to improve chicken meat quality traits, susceptibility to lipid oxidation and sensory traits. The following meat quality traits analyseswere performed: Colour, pH, water holding capacity by conventional (gravimetric methods, pressure application, centrifugation and cooking) and innovative methods (low-field NMR and DSC analysis) ability to absorb marinade soloutions, texture (shear force using different probes and texture profile analysis), proximate analysis (moisture, proteins, lipids, ash content, collagen, fatty acid), susceptibility to lipid oxidation (determinations of reactive substances with thiobarbituric acid and peroxide value), sensorial analysis (triangle test and consumer test).

Farming and processing factors for improving quality properties of poultry and rabbit meat

Recently, global meat market is facing several dramatic changes due to shifting in diet and life style, consumer demands, and economical considerations. Firstly, there was a tremendous increase in the poultry meat demand. Furthermore, current forecast and projection studies pointed out that the expansion of the poultry market will continue in future. In response to this demand, there was a great success to increase growth rate of meat-type chickens in the last few decades in order to optimize the production of poultry meat. Accordingly, the increase of growth rate induced the appearance of several muscle abnormalities such as pale-soft-exudative (PSE) syndrome and deep-pectoral-myopathy (DPM) and more recently white striping and wooden breast. Currently, there is growing interest in meat industry to understand how much the magnitude of the effect of these abnormalities on different quality traits for raw and processed meat. Therefore, the major part of the research activities during the PhD project was dedicated to evaluate the different implications of recent muscle abnormalities such as white striping and wooden breast on meat quality traits and their incidence under commercial conditions. Generally, our results showed that the incidence of these muscle abnormalities was very high under commercial conditions and had great adverse impact on meat quality traits. Secondly, there is growing market share of convenient, healthy, and functional processed meat products. Accordingly, the remaining part of research activities of the PhD project was dedicated to evaluate the possibility to formulate processed meat products with higher perceived healthy profile such as phosphate free-marinated chicken meat and low sodium-marinated rabbit meat products. Overall all findings showed that sodium bicarbonate can be considered as promising component to replace phosphates in meat products, while potassium chloride under certain conditions was successfully used to produce low marinated rabbit meat products.

Non-Equilibrium Atmospheric Plasma As A Novel Route To Nanomaterial Synthesis And Processing For Biomedical Applications

Since last century, the rising interest of value-added and advanced functional materials has spurred a ceaseless development in terms of industrial processes and applications. Among the emerging technologies, thanks to their unique features and versatility in terms of supported processes, non-equilibrium plasma discharges appear as a key solvent-free, high-throughput and cost-efficient technique. Nevertheless, applied research studies are needed with the aim of addressing plasma potentialities optimizing devices and processes for future industrial applications. In this framework, the aim of this dissertation is to report on the activities carried out and the results achieved concerning the development and optimization of plasma techniques for nanomaterial synthesis and processing to be applied in the biomedical field. In the first section, the design and investigation of a plasma assisted process for the production of silver (Ag) nanostructured multilayer coatings exhibiting anti-biofilm and anti-clot properties is described. With the aim on enabling in-situ and on-demand deposition of Ag nanoparticles (NPs), the optimization of a continuous in-flight aerosol process for particle synthesis is reported. The stability and promising biological performances of deposited coatings spurred further investigation through in-vitro and in-vivo tests which results are reported and discussed. With the aim of addressing the unanswered questions and tuning NPs functionalities, the second section concerns the study of silver containing droplet conversion in a flow-through plasma reactor. The presented results, obtained combining different analysis techniques, support a formation mechanism based on droplet to particle conversion driven by plasma induced precursor reduction. Finally, the third section deals with the development of a simulative and experimental approach used to investigate the in-situ droplet evaporation inside the plasma discharge addressing the main contributions to liquid evaporation in the perspective of process industrial scale up.

Deploying and processing neural representations of signals

Neural representations (NR) have emerged in the last few years as a powerful tool to represent signals from several domains, such as images, 3D shapes, or audio. Indeed, deep neural networks have been shown capable of approximating continuous functions that describe a given signal with theoretical infinite resolution. This finding allows obtaining representations whose memory footprint is fixed and decoupled from the resolution at which the underlying signal can be sampled, something that is not possible with traditional discrete representations, e.g., grids of pixels for images or voxels for 3D shapes. During the last two years, many techniques have been proposed to improve the capability of NR to approximate high-frequency details and to make the optimization procedures required to obtain NR less demanding both in terms of time and data requirements, motivating many researchers to deploy NR as the main form of data representation for complex pipelines. Following this line of research, we first show that NR can approximate precisely Unsigned Distance Functions, providing an effective way to represent garments that feature open 3D surfaces and unknown topology. Then, we present a pipeline to obtain in a few minutes a compact Neural Twin® for a given object, by exploiting the recent advances in modeling neural radiance fields. Furthermore, we move a step in the direction of adopting NR as a standalone representation, by considering the possibility of performing downstream tasks by processing directly the NR weights. We first show that deep neural networks can be compressed into compact latent codes. Then, we show how this technique can be exploited to perform deep learning on implicit neural representations (INR) of 3D shapes, by only looking at the weights of the networks.

Embedded systems and advanced signal processing for Acousto- Ultrasonic Inspections

Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) are becoming essential in many application contexts, e.g. civil, industrial, aerospace etc., to reduce structures maintenance costs and improve safety. Conventional inspection methods typically exploit bulky and expensive instruments and rely on highly demanding signal processing techniques. The pressing need to overcome these limitations is the common thread that guided the work presented in this Thesis. In the first part, a scalable, low-cost and multi-sensors smart sensor network is introduced. The capability of this technology to carry out accurate modal analysis on structures undergoing flexural vibrations has been validated by means of two experimental campaigns. Then, the suitability of low-cost piezoelectric disks in modal analysis has been demonstrated. To enable the use of this kind of sensing technology in such non conventional applications, ad hoc data merging algorithms have been developed. In the second part, instead, imaging algorithms for Lamb waves inspection (namely DMAS and DS-DMAS) have been implemented and validated. Results show that DMAS outperforms the canonical Delay and Sum (DAS) approach in terms of image resolution and contrast. Similarly, DS-DMAS can achieve better results than both DMAS and DAS by suppressing artefacts and noise. To exploit the full potential of these procedures, accurate group velocity estimations are required. Thus, novel wavefield analysis tools that can address the estimation of the dispersion curves from SLDV acquisitions have been investigated. An image segmentation technique (called DRLSE) was exploited in the k-space to draw out the wavenumber profile. The DRLSE method was compared with compressive sensing methods to extract the group and phase velocity information. The validation, performed on three different carbon fibre plates, showed that the proposed solutions can accurately determine the wavenumber and velocities in polar coordinates at multiple excitation frequencies.

Adaptive multiscale biological signal processing

Biological processes are very complex mechanisms, most of them being accompanied by or manifested as signals that reflect their essential characteristics and qualities. The development of diagnostic techniques based on signal and image acquisition from the human body is commonly retained as one of the propelling factors in the advancements in medicine and biosciences recorded in the recent past. It is a fact that the instruments used for biological signal and image recording, like any other acquisition system, are affected by non-idealities which, by different degrees, negatively impact on the accuracy of the recording. This work discusses how it is possible to attenuate, and ideally to remove, these effects, with a particular attention toward ultrasound imaging and extracellular recordings. Original algorithms developed during the Ph.D. research activity will be examined and compared to ones in literature tackling the same problems; results will be drawn on the base of comparative tests on both synthetic and in-vivo acquisitions, evaluating standard metrics in the respective field of application. All the developed algorithms share an adaptive approach to signal analysis, meaning that their behavior is not dependent only on designer choices, but driven by input signal characteristics too. Performance comparisons following the state of the art concerning image quality assessment, contrast gain estimation and resolution gain quantification as well as visual inspection highlighted very good results featured by the proposed ultrasound image deconvolution and restoring algorithms: axial resolution up to 5 times better than algorithms in literature are possible. Concerning extracellular recordings, the results of the proposed denoising technique compared to other signal processing algorithms pointed out an improvement of the state of the art of almost 4 dB.

Empirical and fundamental mechanical tests in the evaluation of dough and bread rheological properties

Bread dough and particularly wheat dough, due to its viscoelastic behaviour, is probably the most dynamic and complicated rheological system and its characteristics are very important since they highly affect final products’ textural and sensorial properties. The study of dough rheology has been a very challenging task for many researchers since it can provide numerous information about dough formulation, structure and processing. This explains why dough rheology has been a matter of investigation for several decades. In this research rheological assessment of doughs and breads was performed by using empirical and fundamental methods at both small and large deformation, in order to characterize different types of doughs and final products such as bread. In order to study the structural aspects of food products, image analysis techniques was used for the integration of the information coming from empirical and fundamental rheological measurements. Evaluation of dough properties was carried out by texture profile analysis (TPA), dough stickiness (Chen and Hoseney cell) and uniaxial extensibility determination (Kieffer test) by using a Texture Analyser; small deformation rheological measurements, were performed on a controlled stress–strain rheometer; moreover the structure of different doughs was observed by using the image analysis; while bread characteristics were studied by using texture profile analysis (TPA) and image analysis. The objective of this research was to understand if the different rheological measurements were able to characterize and differentiate the different samples analysed. This in order to investigate the effect of different formulation and processing conditions on dough and final product from a structural point of view. For this aim the following different materials were performed and analysed: - frozen dough realized without yeast; - frozen dough and bread made with frozen dough; - doughs obtained by using different fermentation method; - doughs made by Kamut® flour; - dough and bread realized with the addition of ginger powder; - final products coming from different bakeries. The influence of sub-zero storage time on non-fermented and fermented dough viscoelastic performance and on final product (bread) was evaluated by using small deformation and large deformation methods. In general, the longer the sub-zero storage time the lower the positive viscoelastic attributes. The effect of fermentation time and of different type of fermentation (straight-dough method; sponge-and-dough procedure and poolish method) on rheological properties of doughs were investigated using empirical and fundamental analysis and image analysis was used to integrate this information throughout the evaluation of the dough’s structure. The results of fundamental rheological test showed that the incorporation of sourdough (poolish method) provoked changes that were different from those seen in the others type of fermentation. The affirmative action of some ingredients (extra-virgin olive oil and a liposomic lecithin emulsifier) to improve rheological characteristics of Kamut® dough has been confirmed also when subjected to low temperatures (24 hours and 48 hours at 4°C). Small deformation oscillatory measurements and large deformation mechanical tests performed provided useful information on the rheological properties of samples realized by using different amounts of ginger powder, showing that the sample with the highest amount of ginger powder (6%) had worse rheological characteristics compared to the other samples. Moisture content, specific volume, texture and crumb grain characteristics are the major quality attributes of bread products. The different sample analyzed, “Coppia Ferrarese”, “Pane Comune Romagnolo” and “Filone Terra di San Marino”, showed a decrease of crumb moisture and an increase in hardness over the storage time. Parameters such as cohesiveness and springiness, evaluated by TPA that are indicator of quality of fresh bread, decreased during the storage. By using empirical rheological tests we found several differences among the samples, due to the different ingredients used in formulation and the different process adopted to prepare the sample, but since these products are handmade, the differences could be account as a surplus value. In conclusion small deformation (in fundamental units) and large deformation methods showed a significant role in monitoring the influence of different ingredients used in formulation, different processing and storage conditions on dough viscoelastic performance and on final product. Finally the knowledge of formulation, processing and storage conditions together with the evaluation of structural and rheological characteristics is fundamental for the study of complex matrices like bakery products, where numerous variable can influence their final quality (e.g. raw material, bread-making procedure, time and temperature of the fermentation and baking).

Stone architecture in Carso plateau. Settlement patterns and construction techniques of historic buildings

The use of stone and its types of processing have been very important in the vernacular architecture of the cross-border Carso. In Carso this represents an important legacy of centuries and has a uniform typological characteristic to a great extent. The stone was the main constituent of the local architecture, setting and shaping the human environment, incorporating the history of places through their specific symbolic and constructive language. The primary aim of this research is the recognition of the constructive rules and the values embedded in the Carso rural architecture by use and processing of stone. Central to this investigation is the typological reading, aimed to analyze the constructive language expressed by this legacy, through the analysis of the relationship between type, technique and material.

Methods for integrating machine learning and constrained optimization

In the framework of industrial problems, the application of Constrained Optimization is known to have overall very good modeling capability and performance and stands as one of the most powerful, explored, and exploited tool to address prescriptive tasks. The number of applications is huge, ranging from logistics to transportation, packing, production, telecommunication, scheduling, and much more. The main reason behind this success is to be found in the remarkable effort put in the last decades by the OR community to develop realistic models and devise exact or approximate methods to solve the largest variety of constrained or combinatorial optimization problems, together with the spread of computational power and easily accessible OR software and resources. On the other hand, the technological advancements lead to a data wealth never seen before and increasingly push towards methods able to extract useful knowledge from them; among the data-driven methods, Machine Learning techniques appear to be one of the most promising, thanks to its successes in domains like Image Recognition, Natural Language Processes and playing games, but also the amount of research involved. The purpose of the present research is to study how Machine Learning and Constrained Optimization can be used together to achieve systems able to leverage the strengths of both methods: this would open the way to exploiting decades of research on resolution techniques for COPs and constructing models able to adapt and learn from available data. In the first part of this work, we survey the existing techniques and classify them according to the type, method, or scope of the integration; subsequently, we introduce a novel and general algorithm devised to inject knowledge into learning models through constraints, Moving Target. In the last part of the thesis, two applications stemming from real-world projects and done in collaboration with Optit will be presented.

Content-aware approach for improving biomedical image analysis: an interdisciplinary study series

Biomedicine is a highly interdisciplinary research area at the interface of sciences, anatomy, physiology, and medicine. In the last decade, biomedical studies have been greatly enhanced by the introduction of new technologies and techniques for automated quantitative imaging, thus considerably advancing the possibility to investigate biological phenomena through image analysis. However, the effectiveness of this interdisciplinary approach is bounded by the limited knowledge that a biologist and a computer scientist, by professional training, have of each other’s fields. The possible solution to make up for both these lacks lies in training biologists to make them interdisciplinary researchers able to develop dedicated image processing and analysis tools by exploiting a content-aware approach. The aim of this Thesis is to show the effectiveness of a content-aware approach to automated quantitative imaging, by its application to different biomedical studies, with the secondary desirable purpose of motivating researchers to invest in interdisciplinarity. Such content-aware approach has been applied firstly to the phenomization of tumour cell response to stress by confocal fluorescent imaging, and secondly, to the texture analysis of trabecular bone microarchitecture in micro-CT scans. Third, this approach served the characterization of new 3-D multicellular spheroids of human stem cells, and the investigation of the role of the Nogo-A protein in tooth innervation. Finally, the content-aware approach also prompted to the development of two novel methods for local image analysis and colocalization quantification. In conclusion, the content-aware approach has proved its benefit through building new approaches that have improved the quality of image analysis, strengthening the statistical significance to allow unveiling biological phenomena. Hopefully, this Thesis will contribute to inspire researchers to striving hard for pursuing interdisciplinarity.