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Reference TypeConference Proceedings
Last Update2018: administrator
Metadata Last Update2020: administrator
Citation KeyBergamasco:2018:3DMeOb
Title3D medical objects retrieval approach using SPHARMs descriptor and network flow as similarity measure
DateOct. 29 - Nov. 1, 2018
Access Date2020, Nov. 29
Number of Files1
Size590 KiB
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AuthorBergamasco, Leila Cristina Carneiro
AffiliationUniversity of São Paulo
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2018-08-31 15:43:54 :: -> administrator ::
2020-02-19 03:10:44 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Content TypeExternal Contribution
Tertiary TypeFull Paper
KeywordsCBIR 3D, SPHARMs, medical images, network flows, similairty measure.
AbstractThe data processing to obtain useful information is a trending topic in the computing knowledge domain since we have observed a high demand arising from society for efficient techniques to perform this activity. Spherical Harmonics (SPHARMs) have been widely used in the three-dimensional (3D) object processing domain. Harmonic coefficients generated by this mathematical theory are considered a robust source of information about 3D objects. In parallel, Ford-Fulkerson is a classical method in graph theory that solves network flows problems. In this work we demonstrate the potential of using SPHARMs along with the Ford-Fulkerson method, respectively as descriptor and similarity measure. This article also shows how we adapted the later to transform it into a similarity measure. Our approach has been validated by a 3D medical dataset composed by 3D left ventricle surfaces, some of them presenting Congestive Heart Failure (CHF). The results indicated an average precision of 90%. In addition, the execution time was 65% lower than a descriptor previously tested. With the results obtained we can conclude that our approach, mainly the Ford-Fulkerson adaptation proposed, has a great potential to retrieve 3D medical objects.
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Target File3d-medical-objects.pdf
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Next Higher Units8JMKD3MGPAW/3RPADUS
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