An Efficient Radiographic Image Retrieval System Using Convolutional Neural Network

QC 20161208
Content-Based Medical Image Retrieval (CBMIR) is an important research field in the context of medical data management. In this paper we propose a novel CBMIR system for the automatic retrieval of radiographic images. Our approach employs a Convolutional Neural Network (CNN) to obtain high- level image representations that enable a coarse retrieval of images that are in correspondence to a query image. The retrieved set of images is refined via a non-parametric estimation of putative classes for the query image, which are used to filter out potential outliers in favour of more relevant images belonging to those classes. The refined set of images is finally re-ranked using Edge Histogram Descriptor, i.e. a low-level edge-based image descriptor that allows to capture finer similarities between the retrieved set of images and the query image. To improve the computational efficiency of the system, we employ dimensionality reduction via Principal Component Analysis (PCA). Experiments were carried out to evaluate the effectiveness of the proposed system on medical data from the “Image Retrieval in Medical Applications” (IRMA) benchmark database. The obtained results show the effectiveness of the proposed CBMIR system in the field of medical image retrieval.

Rota Bulò, S. (författare)
Moreno, RodrigoKTH,Medicinsk bildbehandling och visualisering(Swepub:kth)u1osc58y (författare)
Kundu, M.K. (författare)
Smedby, ÖrjanKTH,Medicinsk bildbehandling och visualisering(Swepub:kth)u1vc2uzb (författare)
KTHMedicinsk bildbehandling och visualisering (creator_code:org_t)

Ingår i:2016 23rd International Conference on Pattern Recognition (ICPR): Institute of Electrical and Electronics Engineers (IEEE), s. 3134-31399781509048472

Av författaren/redakt...: Chowdhury, Manis ...; Rota Bulò, S.; Moreno, Rodrigo; Kundu, M.K.; Smedby, Örjan

Om ämnet

TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Medicinteknik; och Medicinsk bildbe ...

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