Multi-scale analysis of lung computed tomography images

A computer-aided detection (CAD) system for the identification of lung internal nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 project. The three modules of our lung CAD system, a segmentation algorithm for lung internal region identification, a multi-scale dot-enhancement filter for nodule candidate selection and a multi-scale neural technique for false positive finding reduction, are described. The results obtained on a dataset of low-dose and thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.Comment: 18 pages, 12 low-resolution figure

Virtual Functions Placement with Time Constraints in Fog Computing: a Matching Theory Perspective

This paper proposes two virtual function (VFs) placement approaches in a Fog domain. The considered solutions formulate a matching game with externalities, aiming at minimizing both the worst application completion time and the number of applications in outage, i.e., the number of applications with an overall completion time greater than a given deadline. The first proposed matching game is established between the VFs set and the Fog Nodes (FNs) set by taking into account the ordered sequence of services (i.e., chain) requested by each application. Conversely, the second proposed method overlooks the applications service chain structure in formulating the VF placement problem, with the aim at lowering the computation complexity without loosing the performance. Furthermore, in order to complete our analysis, the stability of the reached matchings has been theoretically proved for both the proposed solutions. Finally, performance comparisons of the proposed MT approaches with different alternatives are provided to highlight the superior performance of the proposed methods

Transmission of visual data over wireless fading channel in real-time systems based on superposition coding scheme

Real-time visual applications are among the most important requirements in the next generation wireless communication systems. In these applications, the transmitted data comprise different layers with different importance levels based on their influence on the reception quality. Furthermore, the real-time transmission nature of these applications makes them sensitive to data losses and transmission delay. To address these issues, an efficient superposition adaptive modulation and coding system, for the optimal system performance, is proposed in this paper. The proposed system switches its modulation and coding scheme adaptively to select the suitable modulation order and coding rate that best match with the instantaneous channel condition. The channel state information is estimated in receiver and fed back to transmitter. In such method, better performances in both data rate and bit error rate (BER) can be attained. Here, the source data are divided into different priority layers with different importance. Each layer bit stream is sent with specific error protection level against channel corruption. The highest error protection level is assigned to the highest priority layer, and vice versa. The modulated bit streams of all layers are then superimposed together and transmitted via Rayleigh fading channel. At the receiver side, a specific multi-stage decoding receiver is used to reconstruct the source data which demodulates the layers in the order of their priorities. Simulation results show that the proposed system provides up to 18 dB SNR and 46 % data rate gains, respectively, compared to the traditional BPSK scheme at BER of 10−4

A Computer Aided Detection system for mammographic images implemented on a GRID infrastructure

The use of an automatic system for the analysis of mammographic images has proven to be very useful to radiologists in the investigation of breast cancer, especially in the framework of mammographic-screening programs. A breast neoplasia is often marked by the presence of microcalcification clusters and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. In the framework of the GPCALMA (GRID Platform for Computer Assisted Library for MAmmography) project, the co-working of italian physicists and radiologists built a large distributed database of digitized mammographic images (about 5500 images corresponding to 1650 patients) and developed a CAD (Computer Aided Detection) system, able to make an automatic search of massive lesions and microcalcification clusters. The CAD is implemented in the GPCALMA integrated station, which can be used also for digitization, as archive and to perform statistical analyses. Some GPCALMA integrated stations have already been implemented and are currently on clinical trial in some italian hospitals. The emerging GRID technology can been used to connect the GPCALMA integrated stations operating in different medical centers. The GRID approach will support an effective tele- and co-working between radiologists, cancer specialists and epidemiology experts by allowing remote image analysis and interactive online diagnosis.Comment: 5 pages, 5 figures, to appear in the Proceedings of the 13th IEEE-NPSS Real Time Conference 2003, Montreal, Canada, May 18-23 200