Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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A. Shah, S. Conjeti, N. Navab, A. Katouzian
Deeply learnt hashing forests for content based image retrieval in prostate MR images
SPIE Medical Imaging, San Diego, California, USA, March 2016 (bib)

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 uses the semantic descriptiveness of deep learnt Convolutional Neural Networks and parses the feature space using unsupervised random forests to similarity preserving compact binary codewords. Correlation defined on this descriptor is used as an efficient similarity metric for image retrieval.
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