Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion

© 2015 American Association of Physicists in Medicine. Purpose: Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. Methods: The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensors) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. Results: The experimental results demonstrate that the authors proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. Conclusions: A robust electromagnetically guided endoscopy framework was proposed on the basis of an enhanced particle swarm optimization method with using the current observation information and adaptive evolutionary factors. The authors proposed framework greatly reduced the guidance errors from (4.3, 7.8) to (3.0 mm, 5.6°), compared to state-of-the-art methods

A comparison of modified evolutionary computation algorithms with applications to three-dimensional endoscopic camera motion tracking

© 2017 IEEE. Endoscope 3D motion tracking plays an irreplaceable role for computer-assisted endoscopy systems development. Without such tracking, it is impossible to synchronize pre- and intraoperative images in a reference coordinate frame. Currently available methods are comprised of video-based and electromagnetic tracking. These methods limit to either video image artifacts or inaccurate sensor measurements and dynamic errors. This paper proposes two modified evolutionary computation algorithms: (a) adaptive particle swarm optimization (APSO) and (b) observation-boosted differential evolution (OBDE), to augment current endoscopic camera motion tracking. The experimental results demonstrate that our modified algorithms, which combine endoscopic video images with sensor measurements to estimate endoscope movements, can improve tracking accuracy from 4.8 mm to 2.9 mm. OBDE outperforms APSO for endoscope tracking

Observation-driven adaptive differential evolution and its application to accurate and smooth bronchoscope three-dimensional motion tracking

© 2015 Elsevier B.V. This paper proposes an observation-driven adaptive differential evolution algorithm that fuses bronchoscopic video sequences, electromagnetic sensor measurements, and computed tomography images for accurate and smooth bronchoscope three-dimensional motion tracking. Currently an electromagnetic tracker with a position sensor fixed at the bronchoscope tip is commonly used to estimate bronchoscope movements. The large tracking error from directly using sensor measurements, which may be deteriorated heavily by patient respiratory motion and the magnetic field distortion of the tracker, limits clinical applications. How to effectively use sensor measurements for precise and stable bronchoscope electromagnetic tracking remains challenging. We here exploit an observation-driven adaptive differential evolution framework to address such a challenge and boost the tracking accuracy and smoothness. In our framework, two advantageous points are distinguished from other adaptive differential evolution methods: (1) the current observation including sensor measurements and bronchoscopic video images is used in the mutation equation and the fitness computation, respectively and (2) the mutation factor and the crossover rate are determined adaptively on the basis of the current image observation. The experimental results demonstrate that our framework provides much more accurate and smooth bronchoscope tracking than the state-of-the-art methods. Our approach reduces the tracking error from 3.96 to 2.89. mm, improves the tracking smoothness from 4.08 to 1.62. mm, and increases the visual quality from 0.707 to 0.741

Dense feature correspondence for video-based endoscope three-dimensional motion tracking

This paper presents an improved video-based endoscope tracking approach on the basis of dense feature correspondence. Currently video-based methods often fail to track the endoscope motion due to low-quality endoscopic video images. To address such failure, we use image texture information to boost the tracking performance. A local image descriptor - DAISY is introduced to efficiently detect dense texture or feature information from endoscopic images. After these dense feature correspondence, we compute relative motion parameters between the previous and current endoscopic images in terms of epipolar geometric analysis. By initializing with the relative motion information, we perform 2-D/3-D or video-volume registration and determine the current endoscope pose information with six degrees of freedom (6DoF) position and orientation parameters. We evaluate our method on clinical datasets. Experimental results demonstrate that our proposed method outperforms state-of-the-art approaches. The tracking error was significantly reduced from 7.77 mm to 4.78 mm. © 2014 IEEE

Generative recorrupted-to-recorrupted: an unsupervised image denoising network for arbitrary noise distribution

With the great breakthrough of supervised learning in the field of denoising, more and more works focus on end-to-end learning to train denoisers. In practice, however, it can be very challenging to obtain labels in support of this approach. The premise of this method is effective is that there is certain data support, but in practice, it is particularly difficult to obtain labels in the training data. Several unsupervised denoisers have emerged in recent years; however, to ensure their effectiveness, the noise model must be determined in advance, which limits the practical use of unsupervised denoising.n addition, obtaining inaccurate noise prior to noise estimation algorithms leads to low denoising accuracy. Therefore, we design a more practical denoiser that requires neither clean images as training labels nor noise model assumptions. Our method also needs the support of the noise model; the difference is that the model is generated by a residual image and a random mask during the network training process, and the input and target of the network are generated from a single noisy image and the noise model. At the same time, an unsupervised module and a pseudo supervised module are trained. The extensive experiments demonstrate the effectiveness of our framework and even surpass the accuracy of supervised denoising

Modelling local amenities with online open-source data in a new spatial equilibrium model: Insights from applications for Beijing

This paper presents a new, general purpose method for modelling local amenities in a city-level spatial equilibrium model with emerging data sources. A log-form utility function is introduced to differentiate local amenities from other hard-to-observe influences of locational choice for residential and job location. In particular, we use the online open data of schools and hospitals in Beijing to improve model parameterization and calibration at high spatial resolution. The new local amenities element can improve the model’s fidelity on residence location choice by over 30%, which is a step forward in decomposing the zonal attractiveness in spatial equilibrium models. Moreover the local amenities component provides a new interface for the spatial equilibrium models, where quantification of the combined effects of urban land-use and local amenities policies can be simulated on a more consistent basis. The calibrated model of Beijing shows that the coordination of local amenities provision has significant impacts on the performance of urban spatial strategies. Uncoordinated local amenities provision may undermine or even overturn the long-term plans for building a polycentric city region

Improving Ant Collaborative Filtering on Sparsity via Dimension Reduction

Recommender systems should be able to handle highly sparse training data that continues to change over time. Among the many solutions, Ant Colony Optimization, as a kind of optimization algorithm modeled on the actions of an ant colony, enjoys the favorable characteristic of being optimal, which has not been easily achieved by other kinds of algorithms. A recent work adopting genetic optimization proposes a collaborative filtering scheme: Ant Collaborative Filtering (ACF), which models the pheromone of ants for a recommender system in two ways: (1) use the pheromone exchange to model the ratings given by users with respect to items; (2) use the evaporation of existing pheromone to model the evolution of users’ preference change over time. This mechanism helps to identify the users and the items most related, even in the case of sparsity, and can capture the drift of user preferences over time. However, it reveals that many users share the same preference over items, which means it is not necessary to initialize each user with a unique type of pheromone, as was done with the ACF. Regarding the sparsity problem, this work takes one step further to improve the Ant Collaborative Filtering’s performance by adding a clustering step in the initialization phase to reduce the dimension of the rate matrix, which leads to the results that K<<#users, where K is the number of clusters, which stands for the maximum number of types of pheromone carried by all users. We call this revised version the Improved Ant Collaborative Filtering (IACF). Experiments are conducted on larger datasets, compared with the previous work, based on three typical recommender systems: (1) movie recommendations, (2) music recommendations, and (3) book recommendations. For movie recommendation, a larger dataset, MoviesLens 10M, was used, instead of MoviesLens 1M. For book recommendation and music recommendation, we used a new dataset that has a much larger size of samples from Douban and NetEase. The results illustrate that our IACF algorithm can better deal with practical recommendation scenarios that handle sparse dataset

A probabilistic approach for energy efficient data transmission in pipeline monitoring sensor networks

Lifetime network is the main considering problem when deploying wireless sensor networks for integrity monitoring of pipeline infrastructures. And the low network lifetime is always caused by the unbalanced energy consumption in the monitoring networks. So in this paper, a sort of data transmission approach based on probabilistic model is put forward to solve the energy consumption unbalanced and enhance the network lifetime. The optimal problem for maximum network lifetime is introduced, which is solved by artificial fish school algorithm. A series of simulation experiments are carried out to verify the effectiveness of new method. Compared with Direct and Multi-hop methods, new method not only can efficiently balance the network energy load, but also can significantly prolong the network lifetime, meeting the requirements of real-time pipeline monitoring. © 2011 Published by Elsevier Ltd

Complete mitochondrial genome of Prismognathus prossi (Coleoptera: Lucanidae) with phylogenetic implications

The complete mitochondrial genome of a Chinese stag beetle, Prismognathus prossi, was generated using the Illumina next-generation sequencing. The mitogenome sequence is 15,984 bp in length, the nucleotide composition isA 36.6%, C17.5%, T34.3% andG11.6%with theAT-content of 70.9%. The sequence has similar features with other reported insectmitogenomes, consisting of 13 proteincoding genes (PCGs), 22 transferRNAgenes, tworibosomalRNAsand a control region. All of the protein-coding genes start with the typicalATNinitiation codon except for COI. Maximum Likelihood (ML) and Bayesian Inference (BI) indicated that P. prossi share an affinity with Lucanus mazama, Lucanus fortunei and Cyclommatus vitalisi