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21 March 2016 Deeply learnt hashing forests for content based image retrieval in prostate MR images
Amit Shah, Sailesh Conjeti, Nassir Navab, Amin Katouzian
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Deluge in the size and heterogeneity of medical image databases necessitates the need for content based retrieval systems for their efficient organization. In this paper, we propose such a system to retrieve prostate MR images which share similarities in appearance and content with a query image. We introduce deeply learnt hashing forests (DL-HF) for this image retrieval task. DL-HF effectively leverages the semantic descriptiveness of deep learnt Convolutional Neural Networks. This is used in conjunction with hashing forests which are unsupervised random forests. DL-HF hierarchically parses the deep-learnt feature space to encode subspaces with compact binary code words. We propose a similarity preserving feature descriptor called Parts Histogram which is derived from DL-HF. Correlation defined on this descriptor is used as a similarity metric for retrieval from the database. Validations on publicly available multi-center prostate MR image database established the validity of the proposed approach. The proposed method is fully-automated without any user-interaction and is not dependent on any external image standardization like image normalization and registration. This image retrieval method is generalizable and is well-suited for retrieval in heterogeneous databases other imaging modalities and anatomies.
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Amit Shah, Sailesh Conjeti, Nassir Navab, and Amin Katouzian "Deeply learnt hashing forests for content based image retrieval in prostate MR images", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978414 (21 March 2016);

Cited by 15 scholarly publications.
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