Deeply learnt hashing forests for content based image retrieval in prostate MR images

Amit Shah; Sailesh Conjeti; Nassir Navab; Amin Katouzian

doi:10.1117/12.2217162

21 March 2016 Deeply learnt hashing forests for content based image retrieval in prostate MR images

Amit Shah, Sailesh Conjeti, Nassir Navab, Amin Katouzian

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 978414 (2016) https://doi.org/10.1117/12.2217162
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

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.

Citation Download Citation

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); https://doi.org/10.1117/12.2217162

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