Stefano Marrone

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated in recent years a great potential in screening of high-risk women for breast cancer, in staging newly diagnosed patients and in assessing therapy effects. The aim of this work is to propose an automated system for suspicious lesion detection in DCE-MRI to support radiologists during patient image analysis. The proposed method is based on a Support Vector Machine trained with dynamic features, extracted, after a… Visualizza altro

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated in recent years a great potential in screening of high-risk women for breast cancer, in staging newly diagnosed patients and in assessing therapy effects. The aim of this work is to propose an automated system for suspicious lesion detection in DCE-MRI to support radiologists during patient image analysis. The proposed method is based on a Support Vector Machine trained with dynamic features, extracted, after a suitable pre-processing of the image, from an area pre-selected by using a pixel-based approach. The performance were evaluated by using a leave-one-patient-out approach and compared to manual segmentation made up by an experienced radiologist. Our results were also compared to other automatic segmentation methodologies: the proposed method maximises the area of correctly detected lesions while minimizing the number of false alarms (with an accuracy of 98.70%). Meno dettagli

In recent years, the need for data collection and analysis is growing in many scientific disciplines. This is consequently causing an increase of research in automated data management and data mining to create reliable methods for data analysis. To deal with the need for smart environments and big computational resources, some previous works proposed to address the problem by moving on remote processing, with the aim of sharing supercomputer resources, algorithms and costs. Following this… Visualizza altro

In recent years, the need for data collection and analysis is growing in many scientific disciplines. This is consequently causing an increase of research in automated data management and data mining to create reliable methods for data analysis. To deal with the need for smart environments and big computational resources, some previous works proposed to address the problem by moving on remote processing, with the aim of sharing supercomputer resources, algorithms and costs. Following this trend, in this work we propose an architecture for advanced remote data processing in a secure, smart and versatile client–server environment that is capable of integrating pre-existing local software. In order to assess the feasibility of our proposal, we developed a case study in the context of an image-based medical diagnostic environment. Our tests demonstrated that the proposed architecture has several benefits: increase of the system throughput, easy upgradability, maintainability and scalability. Moreover, for the scenario we have considered, the system showed a very low transmission overhead which settles on about 2.5 % for the widespread 10/100 mbps. Security has been achieved using client–server certificates and up-to-date standards. Meno dettagli

It is well known that some sort of motion correction technique (MCT) should be performed before DCE-MRI data analysis in order to reduce movement artefacts. However, it is not clear if a single MCT can produce optimum results for every single examination, since for example different movements can occur. In this paper we investigated the possibility of choosing the best MCT per each specific patient, before performing further data analysis (e.g. tumour segmentation). In particular, our aim is… Visualizza altro

It is well known that some sort of motion correction technique (MCT) should be performed before DCE-MRI data analysis in order to reduce movement artefacts. However, it is not clear if a single MCT can produce optimum results for every single examination, since for example different movements can occur. In this paper we investigated the possibility of choosing the best MCT per each specific patient, before performing further data analysis (e.g. tumour segmentation). In particular, our aim is the proposal of some physiological model-based quality indexes (QIs) for ranking different MCT on a patient basis. Moreover, for practical feasibility, we investigated the performance of our proposal when only a small fraction of the available data was used. We performed tests on a dataset of patients with breast tumour. Specifically, for each patient we compared the “reference ranking” of different MCT obtained by using the results of tumour segmentation with the rankings produced with each QI. Our results indicate that the ranking obtained by using the QI based on the Extended Tofts-Kermode model (with the Parker arterial input function) are in accordance with the “reference ranking”. Moreover, computational load can be significantly reduced without affecting the overall performance by using only 5% of the available data. Meno dettagli

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated in the last decades a great potential in screening of high-risk women for breast cancer, in staging newly diagnosed patients and in assessing therapy effects. The aim of this work is to propose a novel model-based measure for quality evaluation of image registration techniques in DCE-MRI. The proposed measure is based on a compartmental model of blood plasma and of the extra vascular extra cellular space (EES) for… Visualizza altro

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated in the last decades a great potential in screening of high-risk women for breast cancer, in staging newly diagnosed patients and in assessing therapy effects. The aim of this work is to propose a novel model-based measure for quality evaluation of image registration techniques in DCE-MRI. The proposed measure is based on a compartmental model of blood plasma and of the extra vascular extra cellular space (EES) for tumour tissue. Its suitability for evaluating image registration techniques has been indirectly verified by considering the results obtained, after each image registration technique, by a CAD segmentation system, developed in our previous work. In particular, it has been shown that the ranking obtained by means of the proposed quality assessment measure is in agreement with the ranking of the CAD segmentation systems, proving that our measure can correctly evaluate image registration techniques in the DCE-MRI context. Meno dettagli

Up-to-date medical image processing is currently based on very sophisticated algorithms that often require a large computational load not always available on conventional workstations. Moreover, algorithms are in continuous evolution and hence clinicians are typically required to update their workstation periodically. The main objective of this paper is to propose a secure and versatile client-server architecture for providing these services at a low cost. In particular, we developed a plug-in… Visualizza altro

Up-to-date medical image processing is currently based on very sophisticated algorithms that often require a large computational load not always available on conventional workstations. Moreover, algorithms are in continuous evolution and hence clinicians are typically required to update their workstation periodically. The main objective of this paper is to propose a secure and versatile client-server architecture for providing these services at a low cost. In particular, we developed a plug-in allowing OsiriX - a widespread medical image processing application dedicated to DICOM images coming from several equipments - to interact with a system for automatic detection of suspicious lesions in breast DCE-MRI. The large amount of data and the privacy of the information flowing through the network requires a flexible but comprehensive security approach. According to NIST guidelines, in our proposal data are transmitted over SSL/TLS channel after an authentication and authorization procedure based on X.509 standard digital certificate associated with a 3072bit RSA Key Pair. Authentication and authorization procedure is achieved through the services offered by Java JAAS classes. Meno dettagli

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated in recent years a great potential in screening of high-risk women for breast cancer, in staging newly diagnosed patients and in assessing therapy effects. The aim of this work is to propose an automated system for suspicious lesion detection in DCE-MRI to support radiologists during patient image analysis. The proposed method is based on a Support Vector Machine trained with dynamic features, extracted, after a… Visualizza altro

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated in recent years a great potential in screening of high-risk women for breast cancer, in staging newly diagnosed patients and in assessing therapy effects. The aim of this work is to propose an automated system for suspicious lesion detection in DCE-MRI to support radiologists during patient image analysis. The proposed method is based on a Support Vector Machine trained with dynamic features, extracted, after a suitable pre-processing of the image, from an area pre-selected by using a pixel-based approach. The performance were evaluated by using a leave-one-patient-out approach and compared to manual segmentation made up by an experienced radiologist. Our results were also compared to other automatic segmentation methodologies: the proposed method maximises the area of correctly detected lesions while minimizing the number of false alarms (with an accuracy of 98.70%). Meno dettagli