Deeply learnt hashing forests for content based image retrieval in prostate MR images
Shah A, Conjeti S, Navaba N, Katouzian A (2016)
Publication Type: Conference contribution
Publication year: 2016
Journal
Publisher: SPIE
Book Volume: 9784
Conference Proceedings Title: Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Event location: San Diego, CA, USA
ISBN: 9781510600195
DOI: 10.1117/12.2217162
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.
Involved external institutions
Germany (DE)How to cite
APA:
Shah, A., Conjeti, S., Navaba, N., & Katouzian, A. (2016). Deeply learnt hashing forests for content based image retrieval in prostate MR images. In Martin A. Styner, Elsa D. Angelini, Elsa D. Angelini (Eds.), Progress in Biomedical Optics and Imaging - Proceedings of SPIE. San Diego, CA, USA: SPIE.
MLA:
Shah, Amit, et al. "Deeply learnt hashing forests for content based image retrieval in prostate MR images." Proceedings of the Medical Imaging 2016: Image Processing, San Diego, CA, USA Ed. Martin A. Styner, Elsa D. Angelini, Elsa D. Angelini, SPIE, 2016.
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