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

A Fuzzy Logic C-Means Clustering Algorithm to Enhance Microcalcifications Clusters in Digital Mammograms

The detection of microcalcifications is a hard task, since they are quite small and often poorly contrasted against the background of images. The Computer Aided Detection (CAD) systems could be very useful for breast cancer control. In this paper, we report a method to enhance microcalcifications cluster in digital mammograms. A Fuzzy Logic clustering algorithm with a set of features is used for clustering microcalcifications. The method described was tested on simulated clusters of microcalcifications, so that the location of the cluster within the breast and the exact number of microcalcifications is known

Automatic detection of lung nodules in CT datasets based on stable 3D mass–spring models

We propose a computer-aided detection (CAD) system which can detect small-sized (from 3 mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung cancer and their early stage detection arguably improves the patient survival rate. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the full analysis difficult, leading to omission of nodules by the radiologist. We developed an advanced computerized method for the automatic detection of internal and juxtapleural nodules on low-dose and thin-slice lung CT scan. This method consists of an initial selection of nodule candidates list, the segmentation of each candidate nodule and the classification of the features computed for each segmented nodule candidate.The presented CAD system is aimed to reduce the number of omissions and to decrease the radiologist scan examination time. Our system locates with the same scheme both internal and juxtapleural nodules. For a correct volume segmentation of the lung parenchyma, the system uses a Region Growing (RG) algorithm and an opening process for including the juxtapleural nodules. The segmentation and the extraction of the suspected nodular lesions from CT images by a lung CAD system constitutes a hard task. In order to solve this key problem, we use a new Stable 3D Mass–Spring Model (MSM) combined with a spline curves reconstruction process. Our model represents concurrently the characteristic gray value range, the directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. For distinguishing the real nodules among nodule candidates, an additional classification step is applied; furthermore, a neural network is applied to reduce the false positives (FPs) after a double-threshold cut. The system performance was tested on a set of 84 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. The detection rate of the system is 97% with 6.1 FPs/CT. A reduction to 2.5 FPs/CT is achieved at 88% sensitivity. We presented a new 3D segmentation technique for lung nodules in CT datasets, using deformable MSMs. The result is a efficient segmentation process able to converge, identifying the shape of the generic ROI, after a few iterations. Our suitable results show that the use of the 3D AC model and the feature analysis based FPs reduction process constitutes an accurate approach to the segmentation and the classification of lung nodules

Energy resolution and throughput of a new real time digital pulse processing system for x-ray and gamma ray semiconductor detectors

New generation spectroscopy systems have advanced towards digital pulse processing (DPP) approaches. DPP systems, based on direct digitizing and processing of detector signals, have recently been favoured over analog pulse processing electronics, ensuring higher flexibility, stability, lower dead time, higher throughput and better spectroscopic performance. In this work, we present the performance of a new real time DPP system for X-ray and gamma ray semiconductor detectors. The system is based on a commercial digitizer equipped with a custom DPP firmware, developed by our group, for on-line pulse shape and height analysis. X-ray and gamma ray spectra measurements with cadmium telluride (CdTe) and germanium (Ge) detectors, coupled to resistivefeedback preamplifiers, highlight the excellent performance of the system both at low and high rate environments (up to 800 kcps). A comparison with a conventional analog electronics showed the better high-rate capabilities of the digital approach, in terms of energy resolution and throughput. These results make the proposed DPP system a very attractive tool for both laboratory research and for the development of advanced detection systems for high-rate-resolution spectroscopic imaging, recently proposed in diagnostic medicine, industrial imaging and security screening

Detection of bovine papillomavirus type 2 in the peripheral blood of cattle with urinary bladder tumours: possible biological role

Bovine papillomavirus type 2 (BPV-2) infection has been associated with urinary bladder tumours in adult cattle grazing on bracken fern-infested land. In this study, we investigated the simultaneous presence of BPV-2 in whole blood and urinary bladder tumours of adult cattle in an attempt to better understand the biological role of circulating BPV-2. Peripheral blood samples were collected from 78 cattle clinically suffering from a severe chronic enzootic haematuria. Circulating BPV-2 DNA was detected in 61 of them and in two blood samples from healthy cows. Fifty of the affected animals were slaughtered at public slaughterhouses and neoplastic proliferations in the urinary bladder were detected in all of them. BPV-2 DNA was amplified and sequenced in 78% of urinary bladder tumour samples and in 38.9% of normal samples as a control. Circulating episomal BPV-2 DNA was detected in 78.2% of the blood samples. Simultaneous presence of BPV-2 DNA in neoplastic bladder and blood samples was detected in 37 animals. Specific viral E5 mRNA and E5 oncoprotein were also detected in blood by RT-PCR and Western blot/immunocytochemistry, respectively. It is likely that BPV-2 can persist and be maintained in an active status in the bloodstream, in particular in the lymphocytes, as a reservoir of viral infection that, in the presence of co-carcinogens, may cause the development of urinary bladder tumours

HEp-2 Cell Classification with heterogeneous classes-processes based on K-Nearest Neighbours

We present a scheme for the feature extraction and classification of the fluorescence staining patterns of HEp-2 cells in IIF images. We propose a set of complementary processes specific to each class of patterns to search. Our set of processes consists of preprocessing,features extraction and classification. The choice of methods, features and parameters was performed automatically, using the Mean Class Accuracy (MCA) as a figure of merit. We extract a large number (108) of features able to fully characterize the staining pattern of HEp-2 cells. We propose a classification approach based on two steps: the first step follows the one-against-all(OAA) scheme, while the second step follows the one-against-one (OAO) scheme. To do this, we needed to implement 21 KNN classifiers: 6 OAA and 15 OAO. Leave-one-out image cross validation method was used for the evaluation of the results

Privacy and Transparency in Blockchain-based Smart Grid Operations

In the past few years, blockchain technology has emerged in numerous smart grid applications, enabling the construction of systems without the need for a trusted third party. Blockchain offers transparency, traceability, and accountability, which lets various energy management system functionalities be executed through smart contracts, such as monitoring, consumption analysis, and intelligent energy adaptation. Nevertheless, revealing sensitive energy consumption information could render users vulnerable to digital and physical assaults. This paper presents a novel method for achieving a dual balance between privacy and transparency, as well as accountability and verifiability. This equilibrium requires the incorporation of cryptographic tools like Secure Mul- tiparty Computation and Verifiable Secret Sharing within the distributed components of a multi- channel blockchain and its associated smart contracts. We corroborate the suggested architecture throughout the entire process of a Demand Response scenario, from the collection of energy data to the ultimate reward. To address our proposal’s constraints, we present countermeasures against accidental crashes and Byzantine behavior while ensuring that the solution remains appropriate for low-performance IoT devices

Peroxisome proliferator-activated receptor alpha plays a crucial role in behavioral repetition and cognitive flexibility in mice

Acknowledgments We thank Luca Giordano, Giovanni Esposito and Angelo Russo for technical assistance and Dr. Livio Luongo (Second University of Naples–Italy) for critical discussions. This work was supported by a Grant PRIN from Ministry of Education, Universities and Research (MIUR), Italy, to A.C. and the Wellcome Trust (WT098012) to L.K.H. and BBSRC (BB/K001418/1) to L.K.H. and G.D’A. G.D’A. received partial supports from a “FORGIARE” post-doctoral fellowship cofounded by the Polo delle Scienze e Tecnologie per la Vita, University of Naples Federico II and Compagnia di San Paolo Foundation, Turin, Italy (2010–2012).Peer reviewedPublisher PD

Integrated techniques to evaluate the features of sedimentary rocks of archaeological areas of Sicily

Sicily includes a great variety of lithologies, giving a high complexity to the geologic landscape. Their prevalent lithology is sedimentary. It is well known that rocks of sedimentary origin, compared with metamorphic and volcanic deposits, can be relatively soft and hence fairly easy to model. Nevertheless, this workability advantage is a drawback for Cultural Heritage applications. In fact, these materials show a high porosity, with pore-size distributions that lead to deterioration through absorption of water. In this paper, several sedimentary rocks used in historical Cultural Heritage items of Sicily, from "Magna Graecia" to nowadays, are classified for mineralogical features, chemical composition, and for porosity. Particularly, some samples collected in quarries relevant to the archaeological sites of 41 Agrigento, Segesta and Selinunte will be considered and characterized using integrated techniques (XRD, XRF, NMR and CT). Data on samples obtained in laboratory will be compared with the relevant values measured in situ on monuments of historical-cultural interest of the quoted archaeological places