Gaetano Vacca

Image-based detection of Kayser-Fleischer ring in patient with Wilson Disease

In the paper the authors propose an image processing algorithm for detection of Kayser-Fleischer ring in eye cornea. This is a common symptom of a rare genetic disorder known as Wilson Disease. This pathology is cause of a malfunction in the copper excretion from the organism. So copper accumulates in tissues being responsible of oxidative processes in the organs affected. Copper deposit in the cornea is visible as a golden-brown, sometimes orange or greyish, pigmentation called Kayser-Fleischer ring. So it is considered a diagnostic sign of Wilson disease more than ever in individuals with neurological disorders. The proposed algorithm is based on an image processing approach. In detail, eye image is analyzed by means of a segmentation algorithm to detect the Kayser-Fleischer ring. The proposed screening method is non-invasive and automated. The innovative diagnostic tool aims to improve accuracy of actual methods used in practice. So the described technique reduces possible interpretation errors and assists doctor to diagnose the pathology.

Ocular Biometric Measurements to Diagnose Neurological Disorders Due to Wilson Disease

Wilson disease is a pathology because of a gene mutation causing malfunction in the copper excretion from the organism. Therefore, copper accumulation in the body gives rise to oxidative processes. Hence, this rare disease causes several disorders affecting tissues and organs. Neurological disorders lead to copper accumulation in the brain. Approximately 95% of individuals with neurological/psychiatric disorders show a visible symptom in their eyes known as Kayser-Fleischer ring. It is a golden-brown, sometimes orange or greyish, ring because of copper deposit in the cornea. In the medical screening, it is considered as a diagnostic sign of Wilson disease more than ever in individuals with neurological problems. The authors propose an innovative technique based on ocular biometric measurements to diagnose the pathology and the origin of a neurological disorder. An image processing algorithm detects the Kayser-Fleischer ring in the eye cornea through segmentation. Subsequently, biometric measurements provide further information on the severity level of pathology. The aim is to provide a non-invasive diagnostic technique to improve the accuracy of the current methods used in practice and to reduce the possible interpretation errors.

Fluid flow measurements by means of vibration monitoring

The achievement of accurate fluid flow measurements is fundamental whenever the control and the monitoring of certain physical quantities governing an industrial process are required. In that case, non-intrusive devices are preferable, but these are often more sophisticated and expensive than those which are more common (such as nozzles, diaphrams, Coriolis flowmeters and so on). In this paper, a novel, non-intrusive, simple and inexpensive methodology is presented to measure the fluid flow rate (in a turbulent regime) whose physical principle is based on the acquisition of transversal vibrational signals induced by the fluid itself onto the pipe walls it is flowing through. Such a principle of operation would permit the use of micro-accelerometers capable of acquiring and transmitting the signals, even by means of wireless technology, to a control room for the monitoring of the process under control. A possible application (whose feasibility will be investigated by the authors in a further study) of this introduced technology is related to the employment of a net of micro-accelerometers to be installed on pipeline networks of aqueducts. This apparatus could lead to the faster and easier detection and location of possible leaks of fluid affecting the pipeline network with more affordable costs. The authors, who have previously proven the linear dependency of the acceleration harmonics amplitude on the flow rate, here discuss an experimental analysis of this functional relation with the variation in the physical properties of the pipe in terms of its diameter and constituent material, to find the eventual limits to the practical application of the measurement methodology.

A thermal energy balance model for hypotension prevention in hemodialysis

In the present paper, the authors propose a thermal energy balance model in order to prevent accidents in hemodialysis. Hemodialysis is the most common treatment for renal failure. It performs an extracorporeal blood wastes filtration taking the place of the malfunctioning kidney. Nevertheless this replacement therapy is cause of several side effects affecting the hemodynamic stability of patient. The authors focus attention on the hypotension accident. Thermal energy/heat exchanges between extracorporeal system and body might be cause of hypotension occurrence. Unfortunately, today poor importance is given to such aspects. A careful analysis of these issues has allowed authors to define a model for optimizing treatment procedures nowadays used in medical practice. In fact, most of the hemodialysis machines control automatically the dialysate solution temperature starting from peripheral body temperature measurements. Differently, the proposed approach is based on two control parameters: the predialysis patient core temperature and heat exchanges. Non-invasive temperature measurements of arterial and venous blood are obtained by estimating the thermal energy exchange. The aim of the present model is to guarantee a constant patient core temperature preventing intradialytic hypotension.

Observations on the worst case uncertainty

The paper discuss the computation of the worst case uncertainty (WCU) in common measurement problems. The usefulness of computing the WCU besides the standard uncertainty is illustrated. A set of equations to compute the WCU in almost all practical situations is presented. The application of the equations to real-world cases is shown.

An Innovative Strategy for Correctly Interpreting Simultaneous Acquisition of EEG Signals and FMRI Images

Cognitive event-related measurements of the human brain are performed by measuring electrical signals and electromagnetic fields (electroencephalography, EEG, and magnetoencephalography, MEG) and hemodynamic responses (measured by fMRI and PET). The EEG and MEG reflect synchronized electrical activity of neurons, and then show the same timescale as neurocognitive processes. The fMRI is related to the power consumption of groups of neurons and registers a signal on a timescale of several seconds. Unlike fMRI, MEG and EEG are not imaging methods. It is our opinion that the combination of MEG or EEG with the fMRI therefore would be very useful to reach a high resolution, both in time and space, of brain functions. It is not assured however that all measured events during an EEG acquisition and cognitive process-related produce measurable changes of the BOLD signal-and vice versa. In this paper, a new strategy of combining signals (electric and hemodynamic responses) simultaneously acquired from different clinical methodologies is performed and tested in order to produce more reliable information about brain activity. Two different algorithms are explored and compared via repeatability standard deviation estimations of fMRI images.

Energy-based indexes for analysis of vibrations from rotating machinery based on the Hilbert-Huang transform

The aim of the paper is to introduce a parameter indicating a possible progressive deterioration of rotating machinery affected by any dynamic misalignment (due to increasing eccentricities, uneven mass distribution, propagating cracks, etc.). Such parameter, which has to be considered as a damage index, is based on a series of post-processing algorithms to be implemented on the vibration signals acquired on static supporting elements. The vibrations acquired from rotating machinery could be influenced by non-stationary and non-linear features, especially when the rotating elements are affected by dynamical unbalances due to misalignments or uneven mass distribution. The authors use the Hibert-Huang Transform tool, that is able to decompose the original signal into Intrinsic Mode Functions (IMFs), each of them holding quite significant information about the instantaneous frequencies and the energy content of the original signal.

OPTIMIZED PROCEDURE TO EVALUATE THE THERMAL ENERGY TRANSFER IN HEMODIALYSIS TREATMENT

Hemodialysis treatment is biased by complications during its operative phases. One of these potential accidents consists in intradialytic hypotension, that causes discomfort to the patient and may even increase the risk of death. Therefore, it is very important to predict these events in routine clinical procedures. A cause of hypotension may be thermal energy/heat exchanges between extracorporeal system and the surrounding environment. A model to evaluate these heat losses is proposed and improved in order to maintain constant patient blood temperature. A convenient procedure is defined to improve and optimize clinical treatment. Although most hemodialysis machines automatically control the dialysate solution temperature starting from peripheral body temperature measurements, the proposed method is based on the control of the pre-dialysis core temperature of the patient, and the temperature of the blood entering the artery from the extracorporeal circuit after the treatment in the dialyzer. Measurements of arterial and venous blood temperatures are obtained in a non invasive way by a suitable estimate of the thermal energy exchanges between the blood and the environment during the extracorporeal recirculation. The suggested model guarantees a constant core temperature of the patient, improving prevention from intradialytic hypotension.

A Comparison between the Decimated Padé Approximant and Decimated Signal Diagonalization Methods for Leak Detection in Pipelines Equipped with Pressure Sensors

Pipelines conveying fluids are considered strategic infrastructures to be protected and maintained. They generally serve for transportation of important fluids such as drinkable water, waste water, oil, gas, chemicals, etc. Monitoring and continuous testing, especially on-line, are necessary to assess the condition of pipelines. The paper presents findings related to a comparison between two spectral response algorithms based on the decimated signal diagonalization (DSD) and decimated Padé approximant (DPA) techniques that allow to one to process signals delivered by pressure sensors mounted on an experimental pipeline.

On the worst case uncertainty and its evaluation

The paper is a review on the worst case uncertainty (WCU) concept, neglected in the Guide to the Expression of Uncertainty in Measurements (GUM), but necessary for a correct uncertainty assessment in a number of practical cases involving distribution with compact support. First, it is highlighted that the knowledge of the WCU is necessary to choose a sensible coverage factor, associated to a sensible coverage probability: the Maximum Acceptable Coverage Factor (MACF) is introduced as a convenient index to guide this choice. Second, propagation rules for the worst-case uncertainty are provided in matrix and scalar form. It is highlighted that when WCU propagation cannot be computed, the Monte Carlo approach is the only way to obtain a correct expanded uncertainty assessment, in contrast to what can be inferred from the GUM. Third, examples of applications of the formulae to ordinary instruments and measurements are given. Also an example taken from the GUM is discussed, underlining some inconsistencies in it.