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GIOVANNI GIBERTONI

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Pubblicazioni

2023 - Image analysis algorithm for the Anterior Chamber Angle Closure estimation and Van Herick classification [Relazione in Atti di Convegno]
Cassanelli, Davide; Gibertoni, Giovanni; Ferrazza, Manuela; Tramarin, Federico; Tanga, Lucia; Quaranta, Luciano; Oddone, Francesco; Rovati, Luigi
abstract

2023 - SILENT STIMULATION OF CONES: A COMPARISON BETWEEN THE ERG AND PLR RESPONSES [Relazione in Atti di Convegno]
Gibertoni, G.; Irungovel, A. B. P.; Viswanathan, S.; Rovati, L.
abstract

The Electroretinogram measures the overall electrical activity of the retina in response to light stimulation, while the dynamics of Pupillary Light Reflex reveal information about how the visual pathway innervates the iris muscles in response to such stimuli. By simultaneously evaluating PLR and ERG responses, a deeper understanding of both image-forming and non-image-forming mechanisms of the human visual system can be gained. Additionally, ERG and PLR responses to bursts of light are contributed by all primary classes of photoreceptors, including rods, (L-M-S) cones, and intrinsically photosensitive ganglion cells (ipRGCs). This study presents and tests a low-cost, Maxwellian view-based optical setup that can be used to acquire synchronous PLR and ERG recordings with silent stimulation techniques on cones photoreceptors in human subjects.

2022 - A simple Maxwellian optical system to investigate the photoreceptors contribution to pupillary light reflex [Relazione in Atti di Convegno]
Gibertoni, Giovanni; Di Pinto, Valentina; Cattini, Stefano; Tramarin, Federico; Geiser, Martial; Rovati, Luigi
abstract

2022 - Assessment of a Vision-Based Technique for an Automatic Van Herick Measurement System [Articolo su rivista]
Fedullo, T.; Cassanelli, D.; Gibertoni, G.; Tramarin, F.; Quaranta, L.; Riva, I.; Tanga, L.; Oddone, F.; Rovati, L.
abstract

The adoption of Artificial Intelligence methods within the instrumentation and measurements field is nowadays an attractive research area. On the one hand, making machines learn from data how to perform an activity, rather than hard code sequential instructions, is a convenient and effective solution in many modern research areas. On the other hand, AI allows for the compensation of inaccurate or not complete models of specific phenomena or systems. In this context, this paper investigates the possibility to exploit suitable Machine Learning techniques in a vision-based ophthalmic instrument to perform automatic Anterior Chamber Angle (ACA) measurements. In particular, two CNN–based networks have been identified to automatically classify acquired images and select the ones suitable for the Van–Herick procedure. Extensive clinical trials have been conducted by clinicians, from which a realistic and heterogeneous image dataset has been collected. The measurement accuracy of the proposed instrument is derived by extracting measures from the images of the aforementioned dataset, as well as the system performances have been assessed with respect to differences in patients’ eye color. Currently, the ACA measurement procedure is performed manually by appropriately trained medical personnel. For this reason, Machine Learning and Vision–Based techniques may greatly improve both test objectiveness and diagnostic accessibility, by enabling an automatic measurement procedure.

2022 - Design and Performance of a Near-Infrared Spectroscopy Measurement System for In-Field Alfalfa Moisture Measurement [Articolo su rivista]
Gibertoni, G.; Lenzini, N.; Ferrari, L.; Rovati, L.
abstract

Near-infrared spectroscopy (NIRS) is widely used in fruit and vegetable quality evaluations, usually after harvesting. In particular, the moisture content is a key parameter for determining product quality; processing phase, e.g., drying process; and economical value. NIRS methods are wellestablished for laboratory practices where the specimens are properly prepared and measurement conditions are well controlled. On the other hand, it is known that in-field NIRS measurements present several difficulties, as many influencing variables, such as mechanical vibrations, electrical and optical disturbances, and dust or dirt in general, can affect the spectral measurement. In this paper, we propose the design and present the prototype of a NIRS-based measuring system for the rapid determination of the moisture content of bales. The new system uses of a halogen lamp illumination unit to recover water absorption spectral data in the range of 900–1700 nm. The compact stainless steel body makes the instrument portable and easy to transport for rapid in-field MC measurements. The prototype system was characterized and its performance extensively evaluated in a laboratory environment. Finally, a preliminary test was carried out, where the moisture contents of 12 freshly harvested crops samples were measured using the partial least squares (PLSs) regression method. The obtained results show that our prototype system can estimate the alfalfa moisture content information with a coefficient of determination R2 of 0.985 and a root mean square relative error of estimation of 7.1%.

2022 - On the Use of an Hyperspectral Imaging Vision Based Measurement System and Machine Learning for Iris Pigmentation Grading [Relazione in Atti di Convegno]
Fedullo, T.; Masetti, E.; Gibertoni, G.; Tramarin, F.; Rovati, L.
abstract

Nowadays, the ability to derive accurate measurements from images, i.e. the application of vision-based systems to the measurement field, is becoming an attractive research field. In this context, Machine Learning (ML) algorithms can be exploited to smartly and automatically perform the measurement activity. This paper presents an interesting application of ML techniques to an Hyperspectral Imaging System, devoted to the analysis of the iris pigmentation. Indeed, it is proven that the iris pattern evaluation gives a chance for the analysis of both possible loss of sight and future outbreak of several eye diseases. The proposed Vision-Based Measurement system (VBM) allows to illuminate the subject eyes in the spectral range 480 - 900 nm. In particular, the imaging system foresees to take 22 different images of 2048 x 1536 pixels, thus obtaining a spectral resolution of 20 nm and a spatial resolution of 10.7 μm. In this paper, as a first research step, we evaluate the possibility to develop a suitable Machine Learning algorithm to classify the iris color. In particular, the goal is to point out the possible ML techniques that can be employed, the needed dataset and the possible advantages offered by the hyperspectral approach, compared to the conventional visible light imaging.

2022 - Training of an artificial intelligence algorithm for automatic detection of the Van Herick grade [Relazione in Atti di Convegno]
Cassanelli, Davide; Fedullo, Tommaso; Gibertoni, Giovanni; Saporito, Giorgia; Ferrazza, Manuela; Oddone, Francesco; Riva, Ivano; Quaranta, Luciano; Tramarin, Federico; Rovati, Luigi
abstract

2022 - Vision-Based Eye Image Classification for Ophthalmic Measurement Systems [Articolo su rivista]
Gibertoni, Giovanni; Borghi, Guido; Rovati, Luigi
abstract

: The accuracy and the overall performances of ophthalmic instrumentation, where specific analysis of eye images is involved, can be negatively influenced by invalid or incorrect frames acquired during everyday measurements of unaware or non-collaborative human patients and non-technical operators. Therefore, in this paper, we investigate and compare the adoption of several vision-based classification algorithms belonging to different fields, i.e., Machine Learning, Deep Learning, and Expert Systems, in order to improve the performance of an ophthalmic instrument designed for the Pupillary Light Reflex measurement. To test the implemented solutions, we collected and publicly released PopEYE as one of the first datasets consisting of 15 k eye images belonging to 22 different subjects acquired through the aforementioned specialized ophthalmic device. Finally, we discuss the experimental results in terms of classification accuracy of the eye status, as well as computational load analysis, since the proposed solution is designed to be implemented in embedded boards, which have limited hardware resources in computational power and memory size.

2021 - A Machine Learning Approach for a Vision-Based Van-Herick Measurement System [Relazione in Atti di Convegno]
Fedullo, Tommaso; Cassanelli, Davide; Gibertoni, Giovanni; Tramarin, Federico; Quaranta, Luciano; de Angelis, Giovanni; Rovati, Luigi
abstract

2021 - A new screening system for the estimation of ocular anterior chamber angle width [Relazione in Atti di Convegno]
Cassanelli, D.; Gibertoni, G.; Cattini, S.; Quaranta, L.; Riva, I.; Bruttini, C.; de Angelis, G.; Rovati, L.
abstract

Primary Angle Closure Glaucoma occurs more frequently in people with a narrower limbal anterior chamber depth (LACD) condition. Nowadays, clinical gold standard as an assessment technique, i.e. gonioscopy, is invasive and complex, whereas Van Herick (VH) technique is non-invasive, but subjective. The instrument, we propose, can automatically performs the VH procedure using a blue laser line, a piezo-actuator, and an image recognition algorithm embedded on a Raspberry Pi board. Preliminary measurements have been carried out on volunteers, and the results proved the feasibility of our approach. The final instrument unveils a high potential for early-stage diagnosis and screening applications.

2021 - Towards the development of a new model for the oculomotor system [Relazione in Atti di Convegno]
Gibertoni, G.; Cattini, S.; Rovati, L.
abstract

Pupillary light reflex involves many sensory and motor functions of the eye. For this reason, it represents an important emergency diagnostic tool and provides information to assess brain stem function. The pupil system can be considered in terms of input-output black-box behavior: light stimuli can be easily applied to the eyes, and the pupil size can be measured effortlessly and non-invasively. In this paper, a model for short-light-flash-induced transient pupillary light reflex is presented and preliminary experiments designed to test the model features are described. Results confirm that the developed pupillary light reflex model is suitable to describe the pupil oculomotor system exposed to short-light-flashes.