3D medical objects retrieval approach using SPHARMs descriptor and network flow as similarity measure

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.