Automated Stabilization, Enhancement and Capillaries Segmentation in Videocapillaroscopy.

Oral capillaroscopy is a critical and non-invasive technique used to evaluate microcirculation. Its ability to observe small vessels in vivo has generated significant interest in the field. Capillaroscopy serves as an essential tool for diagnosing and prognosing various pathologies, with anatomic-pathological lesions playing a crucial role in their progression. Despite its importance, the utilization of videocapillaroscopy in the oral cavity encounters limitations due to the acquisition setup, encompassing spatial and temporal resolutions of the video camera, objective magnification, and physical probe dimensions. Moreover, the operator's influence during the acquisition process, particularly how the probe is maneuvered, further affects its effectiveness. This study aims to address these challenges and improve data reliability by developing a computerized support system for microcirculation analysis. The designed system performs stabilization, enhancement and automatic segmentation of capillaries in oral mucosal video sequences. The stabilization phase was performed by means of a method based on the coupling of seed points in a classification process. The enhancement process implemented was based on the temporal analysis of the capillaroscopic frames. Finally, an automatic segmentation phase of the capillaries was implemented with the additional objective of quantitatively assessing the signal improvement achieved through the developed techniques. Specifically, transfer learning of the renowned U-net deep network was implemented for this purpose. The proposed method underwent testing on a database with ground truth obtained from expert manual segmentation. The obtained results demonstrate an achieved Jaccard index of 90.1% and an accuracy of 96.2%, highlighting the effectiveness of the developed techniques in oral capillaroscopy. In conclusion, these promising outcomes encourage the utilization of this method to assist in the diagnosis and monitoring of conditions that impact microcirculation, such as rheumatologic or cardiovascular disorders.

Window-Based Energy Selecting X-ray Imaging and Charge Sharing in Cadmium Zinc Telluride Linear Array Detectors for Contaminant Detection.

The spectroscopic and imaging performance of energy-resolved photon counting detectors, based on new sub-millimetre boron oxide encapsulated vertical Bridgman cadmium zinc telluride linear arrays, are presented in this work. The activities are in the framework of the AVATAR X project, planning the development of X-ray scanners for contaminant detection in food industry. The detectors, characterized by high spatial (250 µm) and energy (<3 keV) resolution, allow spectral X-ray imaging with interesting image quality improvements. The effects of charge sharing and energy-resolved techniques on contrast-to-noise ratio (CNR) enhancements are investigated. The benefits of a new energy-resolved X-ray imaging approach, termed window-based energy selecting, in the detection of low- and high-density contaminants are also shown.

Recognizing the Emergent and Submerged Iceberg of the Celiac Disease: ITAMA Project-Global Strategy Protocol.

Coeliac disease (CD) is frequently underdiagnosed with a consequent heavy burden in terms of morbidity and health care costs. Diagnosis of CD is based on the evaluation of symptoms and anti-transglutaminase antibodies IgA (TGA-IgA) levels, with values above a tenfold increase being the basis of the biopsy-free diagnostic approach suggested by present guidelines. This study showcased the largest screening project for CD carried out to date in school children (n=20,000) aimed at assessing the diagnostic accuracy of minimally invasive finger prick point-of-care tests (POCT) which, combined with conventional celiac serology and the aid of an artificial intelligence-based system, may eliminate the need for intestinal biopsy. Moreover, this study delves deeper into the "coeliac iceberg" in an attempt to identify people with disorders who may benefit from a gluten-free diet, even in the absence of gastrointestinal symptoms, abnormal serology and histology. This was achieved by looking for TGA-IgA mucosal deposits in duodenal biopsy. This large European multidisciplinary health project paves the way to an improved quality of life for patients by reducing the costs for diagnosis due to delayed findings of CD and to offer business opportunities in terms of diagnostic tools and support.

Incomplete Charge Collection at Inter-Pixel Gap in Low- and High-Flux Cadmium Zinc Telluride Pixel Detectors.

The success of cadmium zinc telluride (CZT) detectors in room-temperature spectroscopic X-ray imaging is now widely accepted. The most common CZT detectors are characterized by enhanced-charge transport properties of electrons, with mobility-lifetime products µeτe > 10-2 cm2/V and µhτh > 10-5 cm2/V. These materials, typically termed low-flux LF-CZT, are successfully used for thick electron-sensing detectors and in low-flux conditions. Recently, new CZT materials with hole mobility-lifetime product enhancements (µhτh > 10-4 cm2/V and µeτe > 10-3 cm2/V) have been fabricated for high-flux measurements (high-flux HF-CZT detectors). In this work, we will present the performance and charge-sharing properties of sub-millimeter CZT pixel detectors based on LF-CZT and HF-CZT crystals. Experimental results from the measurement of energy spectra after charge-sharing addition (CSA) and from 2D X-ray mapping highlight the better charge-collection properties of HF-CZT detectors near the inter-pixel gaps. The successful mitigation of the effects of incomplete charge collection after CSA was also performed through original charge-sharing correction techniques. These activities exist in the framework of international collaboration on the development of energy-resolved X-ray scanners for medical applications and non-destructive testing in the food industry.

Energy Recovery of Multiple Charge Sharing Events in Room Temperature Semiconductor Pixel Detectors.

Multiple coincidence events from charge-sharing and fluorescent cross-talk are typical drawbacks in room-temperature semiconductor pixel detectors. The mitigation of these distortions in the measured energy spectra, using charge-sharing discrimination (CSD) and charge-sharing addition (CSA) techniques, is always a trade-off between counting efficiency and energy resolution. The energy recovery of multiple coincidence events is still challenging due to the presence of charge losses after CSA. In this work, we will present original techniques able to correct charge losses after CSA even when multiple pixels are involved. Sub-millimeter cadmium-zinc-telluride (CdZnTe or CZT) pixel detectors were investigated with both uncollimated radiation sources and collimated synchrotron X rays, at energies below and above the K-shell absorption energy of the CZT material. These activities are in the framework of an international collaboration on the development of energy-resolved photon counting (ERPC) systems for spectroscopic X-ray imaging up to 150 keV.

Development of new CdZnTe detectors for room-temperature high-flux radiation measurements.

Recently, CdZnTe (CZT) detectors have been widely proposed and developed for room-temperature X-ray spectroscopy even at high fluxes, and great efforts have been made on both the device and the crystal growth technologies. In this work, the performance of new travelling-heater-method (THM)-grown CZT detectors, recently developed at IMEM-CNR Parma, Italy, is presented. Thick planar detectors (3 mm thick) with gold electroless contacts were realised, with a planar cathode covering the detector surface (4.1 mm × 4.1 mm) and a central anode (2 mm × 2 mm) surrounded by a guard-ring electrode. The detectors, characterized by low leakage currents at room temperature (4.7 nA cm-2 at 1000 V cm-1), allow good room-temperature operation even at high bias voltages (>7000 V cm-1). At low rates (200 counts s-1), the detectors exhibit an energy resolution around 4% FWHM at 59.5 keV (241Am source) up to 2200 V, by using commercial front-end electronics (A250F/NF charge-sensitive preamplifier, Amptek, USA; nominal equivalent noise charge of 100 electrons RMS). At high rates (1 Mcounts s-1), the detectors, coupled to a custom-designed digital pulse processing electronics developed at DiFC of University of Palermo (Italy), show low spectroscopic degradations: energy resolution values of 8% and 9.7% FWHM at 59.5 keV (241Am source) were measured, with throughputs of 0.4% and 60% at 1 Mcounts s-1, respectively. An energy resolution of 7.7% FWHM at 122.1 keV (57Co source) with a throughput of 50% was obtained at 550 kcounts s-1 (energy resolution of 3.2% at low rate). These activities are in the framework of an Italian research project on the development of energy-resolved photon-counting systems for high-flux energy-resolved X-ray imaging.

Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project.

Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune diseases in humans, done by mean of Indirect ImmunoFluorescence (IIF) method, and performed by analyzing patterns and fluorescence intensity. This paper introduces the AIDA Project (autoimmunity: diagnosis assisted by computer) developed in the framework of an Italy-Tunisia cross-border cooperation and its preliminary results. A database of interpreted IIF images is being collected through the exchange of images and double reporting and a Gold Standard database, containing around 1000 double reported images, has been settled. The Gold Standard database is used for optimization of a CAD (Computer Aided Detection) solution and for the assessment of its added value, in order to be applied along with an Immunologist as a second Reader in detection of autoantibodies. This CAD system is able to identify on IIF images the fluorescence intensity and the fluorescence pattern. Preliminary results show that CAD, used as second Reader, appeared to perform better than Junior Immunologists and hence may significantly improve their efficacy; compared with two Junior Immunologists, the CAD system showed higher Intensity Accuracy (85,5% versus 66,0% and 66,0%), higher Patterns Accuracy (79,3% versus 48,0% and 66,2%), and higher Mean Class Accuracy (79,4% versus 56,7% and 64.2%).

Fuzzy technique for microcalcifications clustering in digital mammograms.

BACKGROUND: Mammography has established itself as the most efficient technique for the identification of the pathological breast lesions. Among the various types of lesions, microcalcifications are the most difficult to identify since they are quite small (0.1-1.0 mm) and often poorly contrasted against an images background. Within this context, the Computer Aided Detection (CAD) systems could turn out to be very useful in breast cancer control. METHODS: In this paper we present a potentially powerful microcalcifications cluster enhancement method applicable to digital mammograms. The segmentation phase employs a form filter, obtained from LoG filter, to overcome the dependence from target dimensions and to optimize the recognition efficiency. A clustering method, based on a Fuzzy C-means (FCM), has been developed. The described method, Fuzzy C-means with Features (FCM-WF), was tested on simulated clusters of microcalcifications, implying that the location of the cluster within the breast and the exact number of microcalcifications are known.The proposed method has been also tested on a set of images from the mini-Mammographic database provided by Mammographic Image Analysis Society (MIAS) publicly available. RESULTS: The comparison between FCM-WF and standard FCM algorithms, applied on both databases, shows that the former produces better microcalcifications associations for clustering than the latter: with respect to the private and the public database we had a performance improvement of 10% and 5% with regard to the Merit Figure and a 22% and a 10% of reduction of false positives potentially identified in the images, both to the benefit of the FCM-WF. The method was also evaluated in terms of Sensitivity (93% and 82%), Accuracy (95% and 94%), FP/image (4% for both database) and Precision (62% and 65%). CONCLUSIONS: Thanks to the private database and to the informations contained in it regarding every single microcalcification, we tested the developed clustering method with great accuracy. In particular we verified that 70% of the injected clusters of the private database remained unaffected if the reconstruction is performed with the FCM-WF. Testing the method on the MIAS databases allowed also to verify the segmentation properties of the algorithm, showing that 80% of pathological clusters remained unaffected.

Mammographic images segmentation based on chaotic map clustering algorithm.

BACKGROUND: This work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images. The chaotic map clustering algorithm is used to group together similar subsets of image pixels resulting in a medically meaningful partition of the mammography. METHODS: The image is divided into pixels subsets characterized by a set of conveniently chosen features and each of the corresponding points in the feature space is associated to a map. A mutual coupling strength between the maps depending on the associated distance between feature space points is subsequently introduced. On the system of maps, the simulated evolution through chaotic dynamics leads to its natural partitioning, which corresponds to a particular segmentation scheme of the initial mammographic image. RESULTS: The system provides a high recognition rate for small mass lesions (about 94% correctly segmented inside the breast) and the reproduction of the shape of regions with denser micro-calcifications in about 2/3 of the cases, while being less effective on identification of larger mass lesions. CONCLUSIONS: We can summarize our analysis by asserting that due to the particularities of the mammographic images, the chaotic map clustering algorithm should not be used as the sole method of segmentation. It is rather the joint use of this method along with other segmentation techniques that could be successfully used for increasing the segmentation performance and for providing extra information for the subsequent analysis stages such as the classification of the segmented ROI.

Direct measurement of mammographic X-ray spectra with a digital CdTe detection system.

In this work we present a detection system, based on a CdTe detector and an innovative digital pulse processing (DPP) system, for high-rate X-ray spectroscopy in mammography (1-30 keV). The DPP system performs a height and shape analysis of the detector pulses, sampled and digitized by a 14-bit, 100 MHz ADC. We show the results of the characterization of the detection system both at low and high photon counting rates by using monoenergetic X-ray sources and a nonclinical X-ray tube. The detection system exhibits excellent performance up to 830 kcps with an energy resolution of 4.5% FWHM at 22.1 keV. Direct measurements of clinical molybdenum X-ray spectra were carried out by using a pinhole collimator and a custom alignment device. A comparison with the attenuation curves and the half value layer values, obtained from the measured and simulated spectra, from an ionization chamber and from a solid state dosimeter, also shows the accuracy of the measurements. These results make the proposed detection system a very attractive tool for both laboratory research, calibration of dosimeters and advanced quality controls in mammography.

A massive lesion detection algorithm in mammography.

A new algorithm for massive lesion detection in mammography is presented. The algorithm consists in three main steps: 1) reduction of the dimension of the image to be processed through the identification of regions of interest (roi) as candidates for massive lesions; 2) characterization of the RoI by means of suitable feature extraction; 3) pattern classification through supervised neural networks. Suspect regions are detected by searching for local maxima of the pixel grey level intensity. A ring of increasing radius, centered on a maximum, is considered until the mean intensity in the ring decreases to a defined fraction of the maximum. The ROIS thus obtained are described by average, variance, skewness and kurtosis of the intensity distributions at different fractions of the radius. A neural network approach is adopted to classify suspect pathological and healthy pattern. The software has been designed in the framework of the INFN (Istituto Nazionale Fisica Nucleare) research project GPCALMA (Grid Platform for Calma) which recruits physicists and radiologists from different Italian Research Institutions and hospitals to develop software for breast cancer detection.