11,214 research outputs found
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
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Efficient smile detection by Extreme Learning Machine
Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning Machine (ELM). The faces are first detected and a holistic flow-based face registration is applied which does not need any manual labeling or key point detection. Then ELM is used to train the classifier. The proposed smile detector is tested with different feature descriptors on publicly available databases including real-world face images. The comparisons against benchmark classifiers including Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) suggest that the proposed ELM based smile detector in general performs better and is very efficient. Compared to state-of-the-art smile detector, the proposed method achieves competitive results without preprocessing and manual registration
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