%0 Conference Proceedings
%A Bergamasco, Leila Cristina Carneiro,
%@affiliation University of São Paulo
%T 3D medical objects retrieval approach using SPHARMs descriptor and network flow as similarity measure
%B Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)
%D 2018
%E Ross, Arun,
%E Gastal, Eduardo S. L.,
%E Jorge, Joaquim A.,
%E Queiroz, Ricardo L. de,
%E Minetto, Rodrigo,
%E Sarkar, Sudeep,
%E Papa, João Paulo,
%E Oliveira, Manuel M.,
%E Arbeláez, Pablo,
%E Mery, Domingo,
%E Oliveira, Maria Cristina Ferreira de,
%E Spina, Thiago Vallin,
%E Mendes, Caroline Mazetto,
%E Costa, Henrique Sérgio Gutierrez,
%E Mejail, Marta Estela,
%E Geus, Klaus de,
%E Scheer, Sergio,
%S Proceedings
%8 Oct. 29 - Nov. 1, 2018
%J Los Alamitos
%I IEEE Computer Society
%C Foz do Iguaçu, PR, Brazil
%K CBIR 3D, SPHARMs, medical images, network flows, similairty measure.
%X The 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.
%@language en
%3 3d-medical-objects.pdf