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Dictionary learning

M Pérez-Albacete
INTRODUCTION AND OBJECTIVES: We researched the start of urological specialisation in Spain, from the end of the 19th century to the institution of the education system (resident medical intern) to learn about the centres and individuals who created the urological teaching units and training schools in which the first Spanish urologists specialised their training. MATERIAL AND METHODS: We extracted the references from books on the history of urology, from periodic urological publications and from the posters on history submitted to the congresses of the Spanish Urological Association and filled in the data and dates with the Historical Dictionary of Spanish Urologists...
May 11, 2018: Actas Urologicas Españolas
Yang Li, Xiaohai He, Qizhi Teng, Junxi Feng, Xiaohong Wu
A superdimension reconstruction algorithm is used for the reconstruction of three-dimensional (3D) structures of a porous medium based on a single two-dimensional image. The algorithm borrows the concepts of "blocks," "learning," and "dictionary" from learning-based superresolution reconstruction and applies them to the 3D reconstruction of a porous medium. In the neighborhood-matching process of the conventional superdimension reconstruction algorithm, the Euclidean distance is used as a criterion, although it may not really reflect the structural correlation between adjacent blocks in an actual situation...
April 2018: Physical Review. E
Haijun Liu, Jian Cheng, Feng Wang
This paper develops a novel sequential subspace clustering method for sequential data. Inspired by the state-of-the-art methods, ordered subspace clustering, and temporal subspace clustering, we design a novel local temporal regularization term based on the concept of temporal predictability. Through minimizing the short-term variance on historical data, it can recover the temporal smoothness relationships in sequential data. Moreover, we claim that the local temporal regularization is more important than the global structural regularization for a specific task, such as sequential subspace clustering, which leads to a concise minimization objective function...
February 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Homa Foroughi, Nilanjan Ray, Hong Zhang
For an object classification system, the most critical obstacles toward real-world applications are often caused by large intra-class variability, arising from different lightings, occlusion, and corruption, in limited sample sets. Most methods in the literature would fail when the training samples are heavily occluded, corrupted or have significant illumination or viewpoint variations. Besides, most of the existing methods and especially deep learning-based methods, need large training sets to achieve a satisfactory recognition performance...
February 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Peng Bao, Jiliu Zhou, Yi Zhang
Classical total variation (TV) based iterative reconstruction algorithms is effective for the reconstruction of piecewise smooth image, but it causes over-smoothing effect for textured regions in the reconstructed image. To address this problem, this work presents a novel computed tomography (CT) reconstruction method for the few-view problem called the group-sparsity regularization-based simultaneous algebraic reconstruction technique (GSR-SART). Group-based sparse representation, which utilizes the concept of a group as the basic unit of sparse representation instead of a patch, is introduced as the image domain prior regularization term to eliminate the over-smoothing effect...
May 8, 2018: International Journal for Numerical Methods in Biomedical Engineering
Zhi Gao, Mingjie Lao, Yongsheng Sang, Fei Wen, Bharath Ramesh, Ruifang Zhai
Light detection and ranging (LiDAR) sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems...
May 6, 2018: Sensors
Bao Ge, Xiang Li, Xi Jiang, Yifei Sun, Tianming Liu
The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size...
2018: Frontiers in Neuroinformatics
Yu Zhao, Fangfei Ge, Tianming Liu
fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities...
April 26, 2018: Medical Image Analysis
Wai Lok Woo, Bin Gao, Ahmed Bouridane, Bingo Wing-Kuen Ling, Cheng Siong Chin
This paper presents an unsupervised learning algorithm for sparse nonnegative matrix factor time⁻frequency deconvolution with optimized fractional β-divergence. The β-divergence is a group of cost functions parametrized by a single parameter β. The Itakura⁻Saito divergence, Kullback⁻Leibler divergence and Least Square distance are special cases that correspond to β=0, 1, 2, respectively. This paper presents a generalized algorithm that uses a flexible range of β that includes fractional values. It describes a maximization⁻minimization (MM) algorithm leading to the development of a fast convergence multiplicative update algorithm with guaranteed convergence...
April 27, 2018: Sensors
Xiang Zhang, Jiarui Sun, Siwei Ma, Zhouchen Lin, Jian Zhang, Shiqi Wang, Wen Gao
Sparse representation leads to an efficient way to approximately recover a signal by the linear composition of a few bases from a learnt dictionary based on which various successful applications have been achieved. However, in the scenario of data compression, its efficiency and popularity are hindered. It is because of the fact that encoding sparsely distributed coefficients may consume more bits for representing the index of nonzero coefficients. Therefore, introducing an accurate rate constraint in sparse coding and dictionary learning becomes meaningful, which has not been fully exploited in the context of sparse representation...
August 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Junfeng Wu, Fang Dai, Gang Hu, Xuanqin Mou
Excessive radiation exposure in computed tomography (CT) scans increases the chance of developing cancer and has become a major clinical concern. Recently, statistical iterative reconstruction (SIR) with l0-norm dictionary learning regularization has been developed to reconstruct CT images from the low dose and few-view dataset in order to reduce radiation dose. Nonetheless, the sparse regularization term adopted in this approach is l0-norm, which cannot guarantee the global convergence of the proposed algorithm...
April 18, 2018: Journal of X-ray Science and Technology
Jinyang Li, Zhijing Liu, Yong Yao
Sparse representation has been proven to be a very effective technique for various image restoration applications. In this paper, an improved sparse representation based method is proposed to detect and estimate defocus blur of imaging sensors. Considering the fact that the patterns usually vary remarkably across different images or different patches in a single image, it is unstable and time-consuming for sparse representation over an over-complete dictionary. We propose an adaptive domain selection scheme to prelearn a set of compact dictionaries and adaptively select the optimal dictionary to each image patch...
April 8, 2018: Sensors
Ouri Cohen, Bo Zhu, Matthew S Rosen
PURPOSE: Demonstrate a novel fast method for reconstruction of multi-dimensional MR fingerprinting (MRF) data using deep learning methods. METHODS: A neural network (NN) is defined using the TensorFlow framework and trained on simulated MRF data computed with the extended phase graph formalism. The NN reconstruction accuracy for noiseless and noisy data is compared to conventional MRF template matching as a function of training data size and is quantified in simulated numerical brain phantom data and International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom data measured on 1...
April 6, 2018: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
Ara Ghazaryan, Saak V Ovsepian, Vasilis Ntziachristos
Glucose sensing is pursued extensively in biomedical research and clinical practice for assessment of the carbohydrate and fat metabolism as well as in the context of an array of disorders, including diabetes, morbid obesity, and cancer. Currently used methods for real-time glucose measurements are invasive and require access to body fluids, with novel tools and methods for non-invasive sensing of the glucose levels highly desired. In this study, we introduce a near-infrared (NIR) optoacoustic spectrometer for sensing physiological concentrations of glucose within aqueous media and describe the glucose spectra within 850-1,900 nm and various concentration ranges...
2018: Frontiers in Endocrinology
Yi Wang, Qingqing Zheng, Pheng Ann Heng
Robust and effective shape prior modeling from a set of training data remains a challenging task, since the shape variation is complicated, and shape models should preserve local details as well as handle shape noises. To address these challenges, a novel robust projective dictionary learning (RPDL) scheme is proposed in this paper. Specifically, the RPDL method integrates the dimension reduction and dictionary learning into a unified framework for shape prior modeling, which can not only learn a robust and representative dictionary with the energy preservation of the training data, but also reduce the dimensionality and computational cost via the subspace learning...
April 2018: IEEE Transactions on Medical Imaging
Guoqing Wu, Yinsheng Chen, Yuanyuan Wang, Jinhua Yu, Xiaofei Lv, Xue Ju, Zhifeng Shi, Liang Chen, Zhongping Chen
Brain tumors are the most common malignant neurologic tumors with the highest mortality and disability rate. Because of the delicate structure of the brain, the clinical use of several commonly used biopsy diagnosis is limited for brain tumors. Radiomics is an emerging technique for noninvasive diagnosis based on quantitative medical image analyses. However, current radiomics techniques are not standardized regarding feature extraction, feature selection, and decision making. In this paper, we propose a sparse representation-based radiomics (SRR) system for the diagnosis of brain tumors...
April 2018: IEEE Transactions on Medical Imaging
Shuting Han, Ekaterina Taralova, Christophe Dupre, Rafael Yuste
Animal behavior has been studied for centuries, but few efficient methods are available to automatically identify and classify behavior. Quantitative behavioral studies have been hindered by the subjective and imprecise nature of human observation, the limitation of human vision and the slow speed of annotating behavioral data. Here we developed an automatic behavior analysis pipeline for the cnidarian Hydra vulgaris using machine learning approaches. We imaged freely behaving Hydra , extracted motion and shape features from the videos, and constructed a dictionary of visual features to classify pre-defined behaviors...
March 28, 2018: ELife
Murad Megjhani, Kalijah Terilli, Hans-Peter Frey, Angela G Velazquez, Kevin William Doyle, Edward Sander Connolly, David Jinou Roh, Sachin Agarwal, Jan Claassen, Noemie Elhadad, Soojin Park
Purpose: Accurate prediction of delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) can be critical for planning interventions to prevent poor neurological outcome. This paper presents a model using convolution dictionary learning to extract features from physiological data available from bedside monitors. We develop and validate a prediction model for DCI after SAH, demonstrating improved precision over standard methods alone. Methods: 488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included...
2018: Frontiers in Neurology
Tian Tian, Chang Li, Jinkang Xu, Jiayi Ma
Detecting urban areas from very high resolution (VHR) remote sensing images plays an important role in the field of Earth observation. The recently-developed deep convolutional neural networks (DCNNs), which can extract rich features from training data automatically, have achieved outstanding performance on many image classification databases. Motivated by this fact, we propose a new urban area detection method based on DCNNs in this paper. The proposed method mainly includes three steps: (i) a visual dictionary is obtained based on the deep features extracted by pre-trained DCNNs; (ii) urban words are learned from labeled images; (iii) the urban regions are detected in a new image based on the nearest dictionary word criterion...
March 18, 2018: Sensors
Yawen Huang, Ling Shao, Alejandro F Frangi
Multi-modality medical imaging is increasingly used for comprehensive assessment of complex diseases in either diagnostic examinations or as part of medical research trials. Different imaging modalities provide complementary information about living tissues. However, multi-modal examinations are not always possible due to adversary factors, such as patient discomfort, increased cost, prolonged scanning time, and scanner unavailability. In additionally, in large imaging studies, incomplete records are not uncommon owing to image artifacts, data corruption or data loss, which compromise the potential of multi-modal acquisitions...
March 2018: IEEE Transactions on Medical Imaging
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