Abstract--Breast cancer is the most frequently diagnosed non-skin cancer in women, the second leading cause of death among women. Early detection of a breast cancer is fundamental for ensuring high survival rate. Imaging techniques are used to identify suspicious modifications of breast tissue. Among these, contrast-enhanced magnetic resonance imaging (CE-MRI) is particularly interesting for its lack of exposure to radiation and its ability to highlight differences in vascularisation, typical of cancer lesions. Automatic or semi-automatic methods are especially useful with this technique, due to the high quantity of data, in the form of 4D images (3D space + time), to be analysed in each test. This survey describes approaches to fully automatic computer-aided detection/diagnosis of breast lesions with CE-MRI, with particular emphasis on computational intelligence techniques.
Automatic Approaches for CE-MRI Examination of the Breast: A Survey
FA Cardillo;
2017
Abstract
Abstract--Breast cancer is the most frequently diagnosed non-skin cancer in women, the second leading cause of death among women. Early detection of a breast cancer is fundamental for ensuring high survival rate. Imaging techniques are used to identify suspicious modifications of breast tissue. Among these, contrast-enhanced magnetic resonance imaging (CE-MRI) is particularly interesting for its lack of exposure to radiation and its ability to highlight differences in vascularisation, typical of cancer lesions. Automatic or semi-automatic methods are especially useful with this technique, due to the high quantity of data, in the form of 4D images (3D space + time), to be analysed in each test. This survey describes approaches to fully automatic computer-aided detection/diagnosis of breast lesions with CE-MRI, with particular emphasis on computational intelligence techniques.| Campo DC | Valore | Lingua |
|---|---|---|
| dc.authority.people | FA Cardillo | it |
| dc.authority.people | F Masulli | it |
| dc.authority.people | S Rovetta | it |
| dc.collection.id.s | 71c7200a-7c5f-4e83-8d57-d3d2ba88f40d | * |
| dc.collection.name | 04.01 Contributo in Atti di convegno | * |
| dc.contributor.appartenenza | Istituto di linguistica computazionale "Antonio Zampolli" - ILC | * |
| dc.contributor.appartenenza.mi | 918 | * |
| dc.date.accessioned | 2024/02/21 03:34:58 | - |
| dc.date.available | 2024/02/21 03:34:58 | - |
| dc.date.issued | 2017 | - |
| dc.description.abstracteng | Abstract--Breast cancer is the most frequently diagnosed non-skin cancer in women, the second leading cause of death among women. Early detection of a breast cancer is fundamental for ensuring high survival rate. Imaging techniques are used to identify suspicious modifications of breast tissue. Among these, contrast-enhanced magnetic resonance imaging (CE-MRI) is particularly interesting for its lack of exposure to radiation and its ability to highlight differences in vascularisation, typical of cancer lesions. Automatic or semi-automatic methods are especially useful with this technique, due to the high quantity of data, in the form of 4D images (3D space + time), to be analysed in each test. This survey describes approaches to fully automatic computer-aided detection/diagnosis of breast lesions with CE-MRI, with particular emphasis on computational intelligence techniques. | - |
| dc.description.affiliations | Istituto di Linguistica Computazionale, CNR, Pisa, Italia. DIBRIS, Università di Genova, Italia DIBRIS, Università di Genova, Italia | - |
| dc.description.allpeople | F.A. Cardillo; F. Masulli; S. Rovetta | - |
| dc.description.allpeopleoriginal | F.A. Cardillo, F. Masulli, S. Rovetta | - |
| dc.description.fulltext | none | en |
| dc.description.numberofauthors | 1 | - |
| dc.identifier.isi | WOS:000426972400021 | - |
| dc.identifier.scopus | 2-s2.0-85047425977 | - |
| dc.identifier.uri | https://hdl.handle.net/20.500.14243/339525 | - |
| dc.language.iso | eng | - |
| dc.relation.conferencedate | 21-23/06/2017 | - |
| dc.relation.conferencename | 10th IEEE International Conference on Cyber, Physical and Social Computing (CPSCom-2017) | - |
| dc.relation.conferenceplace | United Kingdom | - |
| dc.subject.keywords | medical image analysis | - |
| dc.subject.keywords | computational intelligence | - |
| dc.subject.singlekeyword | medical image analysis | * |
| dc.subject.singlekeyword | computational intelligence | * |
| dc.title | Automatic Approaches for CE-MRI Examination of the Breast: A Survey | en |
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| dc.type.miur | 273 | - |
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| iris.isi.extIssued | 2017 | - |
| iris.isi.extTitle | Automatic Approaches for CE-MRI Examination of the Breast: A Survey | - |
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| iris.scopus.extTitle | Automatic Approaches for CE-MRI Examination of the Breast: A Survey | - |
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| isi.contributor.affiliation | Consiglio Nazionale delle Ricerche (CNR) | - |
| isi.contributor.affiliation | University of Genoa | - |
| isi.contributor.affiliation | University of Genoa | - |
| isi.contributor.country | Italy | - |
| isi.contributor.country | Italy | - |
| isi.contributor.country | Italy | - |
| isi.contributor.name | Franco Alberto | - |
| isi.contributor.name | Francesco | - |
| isi.contributor.name | Stefano | - |
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| isi.contributor.surname | Cardillo | - |
| isi.contributor.surname | Masulli | - |
| isi.contributor.surname | Rovetta | - |
| isi.date.issued | 2017 | * |
| isi.description.abstracteng | Breast cancer is the most frequently diagnosed non skin cancer in women, the second leading cause of death among women. Early detection of a breast cancer is fundamental for ensuring high survival rate. Imaging techniques are used to identify suspicious modifications of breast tissue. Among these, contrast enhanced magnetic resonance imaging (CE-MRI) is particularly interesting for its lack of exposure to radiation and its ability to highlight differences in vascularisation, typical of cancer lesions. Automatic or semi-automatic methods are especially useful with this technique, due to the high quantity of data, in the form of 4D images (3D space + time), to be analysed in each test. This survey describes approaches to fully automatic computer aided detection/diagnosis of breast lesions with CE-MRI, with particular emphasis on computational intelligence techniques. | * |
| isi.description.allpeopleoriginal | Cardillo, FA; Masulli, F; Rovetta, S; | * |
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| isi.document.type | Proceedings Paper | * |
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| isi.identifier.doi | 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.27 | * |
| isi.identifier.isi | WOS:000426972400021 | * |
| isi.journal.journaltitle | 2017 IEEE INTERNATIONAL CONFERENCE ON INTERNET OF THINGS (ITHINGS) AND IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) AND IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING (CPSCOM) AND IEEE SMART DATA (SMARTDATA) | * |
| isi.language.original | English | * |
| isi.publisher.place | 345 E 47TH ST, NEW YORK, NY 10017 USA | * |
| isi.relation.firstpage | 147 | * |
| isi.relation.lastpage | 154 | * |
| isi.title | Automatic Approaches for CE-MRI Examination of the Breast: A Survey | * |
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| scopus.contributor.affiliation | University of Genoa | - |
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| scopus.contributor.country | Italy | - |
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| scopus.contributor.dptid | - | |
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| scopus.contributor.name | Franco Alberto | - |
| scopus.contributor.name | Francesco | - |
| scopus.contributor.name | Stefano | - |
| scopus.contributor.subaffiliation | Ist. Linguistica Computazionale; | - |
| scopus.contributor.subaffiliation | DIBRIS; | - |
| scopus.contributor.subaffiliation | DIBRIS; | - |
| scopus.contributor.surname | Cardillo | - |
| scopus.contributor.surname | Masulli | - |
| scopus.contributor.surname | Rovetta | - |
| scopus.date.issued | 2017 | * |
| scopus.description.abstracteng | Breast cancer is the most frequently diagnosed non-skin cancer in women, the second leading cause of death among women. Early detection of a breast cancer is fundamental for ensuring high survival rate. Imaging techniques are used to identify suspicious modifications of breast tissue. Among these, contrast-enhanced magnetic resonance imaging (CE-MRI) is particularly interesting for its lack of exposure to radiation and its ability to highlight differences in vascularisation, typical of cancer lesions. Automatic or semi-automatic methods are especially useful with this technique, due to the high quantity of data, in the form of 4D images (3D space + time), to be analysed in each test. This survey describes approaches to fully automatic computer-aided detection/diagnosis of breast lesions with CE-MRI, with particular emphasis on computational intelligence techniques. | * |
| scopus.description.allpeopleoriginal | Cardillo F.A.; Masulli F.; Rovetta S. | * |
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| scopus.identifier.doi | 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.27 | * |
| scopus.identifier.isbn | 9781538630655 | * |
| scopus.identifier.pui | 622183797 | * |
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| scopus.language.iso | eng | * |
| scopus.publisher.name | Institute of Electrical and Electronics Engineers Inc. | * |
| scopus.relation.conferencedate | 2017 | * |
| scopus.relation.conferencename | Joint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017 | * |
| scopus.relation.conferenceplace | gbr | * |
| scopus.relation.firstpage | 147 | * |
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| scopus.relation.volume | 2018- | * |
| scopus.title | Automatic Approaches for CE-MRI Examination of the Breast: A Survey | * |
| scopus.titleeng | Automatic Approaches for CE-MRI Examination of the Breast: A Survey | * |
| Appare nelle tipologie: | 04.01 Contributo in Atti di convegno | |
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