Evaluation of Statistical Features for Medical Image Retrieval

In this paper we present a complete system allowing the classification of medical images in order to detect possible diseases present in them. The proposed method is developed in two distinct stages: calculation of descriptors and their classification. In the first stage we compute a vector of thirty-three statistical features: seven are related to statistics of the first level order, fifteen to that of second level where thirteen are calculated by means of co-occurrence matrices and two with absolute gradient; the last thirteen finally are calculated using run-length matrices. In the second phase, using the descriptors already calculated, there is the actual image classification. Naive Bayes, RBF, Support VectorMa- chine, K-Nearest Neighbor, Random Forest and Random Tree classifiers are used. The results obtained from the proposed system show that the analysis carried out both on textured and on medical images lead to have a high accuracy

Statistical Features for Image Retrieval: A Quantitative Comparison

In this paper we present a comparison between various statistical descriptors and analyze their goodness in classifying textural images. The chosen statistical descriptors have been proposed by Tamura, Battiato and Haralick. In this work we also test a combination of the three descriptors for texture analysis. The databases used in our study are the well-known Brodatz’s album and DDSM(Heath et al., 1998). The computed features are classified using the Naive Bayes, the RBF, the KNN, the Random Forest and Random Tree models. The results obtained from this study show that we can achieve a high classification accuracy if the descriptors are used all together

Automatic Monitoring Cheese Ripeness Using Computer Vision and Artificial Intelligence

Ripening is a very important process that contributes to cheese quality, as its characteristics are determined by the biochemical changes that occur during this period. Therefore, monitoring ripening time is a fundamental task to market a quality product in a timely manner. However, it is difficult to accurately determine the degree of cheese ripeness. Although some scientific methods have also been proposed in the literature, the conventional methods adopted in dairy industries are typically based on visual and weight control. This study proposes a novel approach aimed at automatically monitoring the cheese ripening based on the analysis of cheese images acquired by a photo camera. Both computer vision and machine learning techniques have been used to deal with this task. The study is based on a dataset of 195 images (specifically collected from an Italian dairy industry), which represent Pecorino cheese forms at four degrees of ripeness. All stages but the one labeled as 'day 18', which has 45 images, consist of 50 images. These images have been handled with image processing techniques and then classified according to the degree of ripening, i.e., 18, 22, 24, and 30 days. A 5-fold cross-validation strategy was used to empirically evaluate the performance of the models. During this phase, each training fold was augmented online. This strategy allowed to use 624 images for training, leaving 39 original images per fold for testing. Experimental results have demonstrated the validity of the approach, showing good performance for most of the trained models

VISUOMOTOR INTEGRATION SKILLS IN CHILDREN AFFECTED BY OBSTRUCTIVE SLEEP APNEA SYNDROME: A CASE-CONTROL STUDY

Introduction: Sleep related breathing disorders (SRBD) consist of frequent and repetitive episodes of pharyngeal obstruction during sleep, with consequent intermittent hypoxia, sleep architecture fragmentation, daytime sleepiness and/or behavioural problems and executive impairment in children. When untreated, SRBD and obstructive sleep apnea syndrome (OSA) mainly, may impact school performance, cognition, metabolism, and cardiovascular function. Aim of the present study is assessing the visuomotor integration skills in children affected by OSA. Materials and methods: 57 subjects affected by mild-to severe OSA, PSG diagnosed according to international diagnostic criteria, (31 males and 26 females) (mean age 10.8; SD \ub1 2.49) and 83 healthy children (45 males and 38 females) (mean age 9.95; SD \ub1 1.87; p = 0.725). All subjects underwent assessment of motor coordination skills with Movement-ABC tests and visual-motor integration ability with Visual Motor Integration (VMI) test. Results: The subjects with OSA show a worse average performances in all items of Movement ABC (p <0.001) respect of controls. Specifically, children with OSAS show significantly higher values of total points (p <0.001), manual dexterity (p <0.001), ball skills (p <0.001) and balance (p <0.001). Accordingly, the average centile in OSA children at the MABC-test is significantly reduced compared with controls (p <0.001). (Table 1) On the other hand, the VMI test evaluation among children with OSAS shows worst result in total Visuo-Motor Integration (p <0.001), and in Motor Coordination sub-item (p <0.001) than controls. (Table 1). Conclusion: Our results also support for children and adolescents the hypothesis that executive functioning deficits might be linked primarily to the degree of severity nocturnal hypoxemia rather than daytime sleepiness, although several other studies are needed

Safety of polymyxin-B-based hemoperfusion in kidney and liver transplant recipients.

Abstract Infection represents one of the primary barriers to successful organ transplantation. Our principal end point was to use a new assay, Entotoxin Activity Assay (EAA), which was developed to rapidly detect endotoxin activity (EA) for an early diagnosis of this complication. We also sought to prove the validity and safety of endotoxin removal using polymyxin-B–based hemoperfusion (PMX-DHP). The criterion for inclusion in the study was suspected infection when a patient experienced at least 2 of the 4 criteria of the systemic inflammatory response syndrome. EAA was performed on 71 patients: 29 liver transplantations and 42 kidney transplantations. Twenty-eight patients (39.5%) with EA >0.60 underwent PMX-DHP treatment to remove endotoxins. Each treatment was performed for 2 hours with a blood flow of 100 mL/min. All of the patients were treated with PMX-DHP until achieving an E

Superinfections in patients treated with teicoplanin as anti SARSCoV2 AGENT

We read with interest the paper by Giacobbe et al. estimating a cumulative risk of developing at least one bloodstream infection (BSI) episode (largely due to Gram-positive pathogens) of almost 50% after 30 days at risk in (2) Similarly, Somers et al. reported an increased risk to develop bacterial superinfections, principally represented by Staphylococcus aureus ventilatory associated pneumonia (VAP), in critically ill patients infected with SARS-CoV-2 and treated with Tocilizumab. (1) We previously described a cohort of intubated patients affected by severe COVID-19pneumonia treated with the best available therapy (BAT), including Tocilizumab and associated with Teicoplanin. (3) This glycopeptide antibiotic was used with a double purpose: as antiviral agent for COVID-19 and as of possible S.aureus superinfection