Learning Deep Similarity Metric for 3D MR-TRUS Registration

Purpose: The fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images for guiding targeted prostate biopsy has significantly improved the biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image registration. However, it is very challenging to obtain a robust automatic MR-TRUS registration due to the large appearance difference between the two imaging modalities. The work presented in this paper aims to tackle this problem by addressing two challenges: (i) the definition of a suitable similarity metric and (ii) the determination of a suitable optimization strategy. Methods: This work proposes the use of a deep convolutional neural network to learn a similarity metric for MR-TRUS registration. We also use a composite optimization strategy that explores the solution space in order to search for a suitable initialization for the second-order optimization of the learned metric. Further, a multi-pass approach is used in order to smooth the metric for optimization. Results: The learned similarity metric outperforms the classical mutual information and also the state-of-the-art MIND feature based methods. The results indicate that the overall registration framework has a large capture range. The proposed deep similarity metric based approach obtained a mean TRE of 3.86mm (with an initial TRE of 16mm) for this challenging problem. Conclusion: A similarity metric that is learned using a deep neural network can be used to assess the quality of any given image registration and can be used in conjunction with the aforementioned optimization framework to perform automatic registration that is robust to poor initialization.Comment: To appear on IJCAR

Using Apache Lucene to Search Vector of Locally Aggregated Descriptors

Surrogate Text Representation (STR) is a profitable solution to efficient similarity search on metric space using conventional text search engines, such as Apache Lucene. This technique is based on comparing the permutations of some reference objects in place of the original metric distance. However, the Achilles heel of STR approach is the need to reorder the result set of the search according to the metric distance. This forces to use a support database to store the original objects, which requires efficient random I/O on a fast secondary memory (such as flash-based storages). In this paper, we propose to extend the Surrogate Text Representation to specifically address a class of visual metric objects known as Vector of Locally Aggregated Descriptors (VLAD). This approach is based on representing the individual sub-vectors forming the VLAD vector with the STR, providing a finer representation of the vector and enabling us to get rid of the reordering phase. The experiments on a publicly available dataset show that the extended STR outperforms the baseline STR achieving satisfactory performance near to the one obtained with the original VLAD vectors.Comment: In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP, p. 383-39

Sparse spatial selection for novelty-based search result diversification

Abstract. Novelty-based diversification approaches aim to produce a diverse ranking by directly comparing the retrieved documents. However, since such approaches are typically greedy, they require O(n 2) documentdocument comparisons in order to diversify a ranking of n documents. In this work, we propose to model novelty-based diversification as a similarity search in a sparse metric space. In particular, we exploit the triangle inequality property of metric spaces in order to drastically reduce the number of required document-document comparisons. Thorough experiments using three TREC test collections show that our approach is at least as effective as existing novelty-based diversification approaches, while improving their efficiency by an order of magnitude.

SPLX-Perm: A Novel Permutation-Based Representation for Approximate Metric Search

Many approaches for approximate metric search rely on a permutation-based representation of the original data objects. The main advantage of transforming metric objects into permutations is that the latter can be efficiently indexed and searched using data structures such as inverted-files and prefix trees. Typically, the permutation is obtained by ordering the identifiers of a set of pivots according to their distances to the object to be represented. In this paper, we present a novel approach to transform metric objects into permutations. It uses the object-pivot distances in combination with a metric transformation, called n-Simplex projection. The resulting permutation-based representation , named SPLX-Perm, is suitable only for the large class of metric space satisfying the n-point property. We tested the proposed approach on two benchmarks for similarity search. Our preliminary results are encouraging and open new perspectives for further investigations on the use of the n-Simplex projection for supporting permutation-based indexing

Metric Embedding into the Hamming Space with the n-Simplex Projection

Transformations of data objects into the Hamming space are often exploited to speed-up the similarity search in metric spaces. Techniques applicable in generic metric spaces require expensive learning, e.g., selection of pivoting objects. However, when searching in common Euclidean space, the best performance is usually achieved by transformations specifically designed for this space. We propose a novel transformation technique that provides a good trade-off between the applicability and the quality of the space approximation. It uses the n-Simplex projection to transform metric objects into a low-dimensional Euclidean space, and then transform this space to the Hamming space. We compare our approach theoretically and experimentally with several techniques of the metric embedding into the Hamming space. We focus on the applicability, learning cost, and the quality of search space approximation

Search Space Reduction in Exemplar Based Image Inpainting

This paper aims at developing accelerated exemplary inpaint method. The feature set is considered to be the pixels along with their 8-neighbors. A Multi Phase Search Space Reduction framework namely Systematic Reduction of Information System (SRIS) is employed. SRIS, basically is a roughest based approach which imputes the missing values in an adaptive manner. In this approach the order of inpainting pixels is determined by a simple but effective priority term. The best exemplar is determined based on a similarity metric which is derived by element wise difference of informative pixels of inpaint window and the corresponding pixels of the source region window