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Dictionary learning

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
Yasmeen George, Mohammad Aldeen, Rahil Garnavi
Psoriasis is a chronic skin disease which can be life-threatening. Accurate severity scoring helps dermatologists to decide on the treatment. In this paper, we present a semi-supervised computer-aided system for automatic erythema severity scoring in psoriasis images. Firstly, the unsupervised stage includes a novel image representation method. We construct a dictionary, which is then used in the sparse representation for local feature extraction. To acquire the final image representation vector, an aggregation method is exploited over the local features...
February 23, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
Ajin Joy, Joseph Suresh Paul
PURPOSE: Avoid formation of staircase artifacts in nonlinear diffusion-based MR image reconstruction without compromising computational speed. METHODS: Whereas second-order diffusion encourages the evolution of pixel neighborhood with uniform intensities, fourth-order diffusion considers smooth region to be not necessarily a uniform intensity region but also a planar region. Therefore, a controlled application of fourth-order diffusivity function is used to encourage second-order diffusion to reconstruct the smooth regions of the image as a plane rather than a group of blocks, while not being strong enough to introduce the undesirable speckle effect...
March 7, 2018: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
Varsha D Badal, Petras J Kundrotas, Ilya A Vakser
BACKGROUND: Structural modeling of protein-protein interactions produces a large number of putative configurations of the protein complexes. Identification of the near-native models among them is a serious challenge. Publicly available results of biomedical research may provide constraints on the binding mode, which can be essential for the docking. Our text-mining (TM) tool, which extracts binding site residues from the PubMed abstracts, was successfully applied to protein docking (Badal et al...
March 5, 2018: BMC Bioinformatics
Valeriya Naumova, Karin Schnass
This paper extends the recently proposed and theoretically justified iterative thresholding and K residual means (ITKrM) algorithm to learning dictionaries from incomplete/masked training data (ITKrMM). It further adapts the algorithm to the presence of a low-rank component in the data and provides a strategy for recovering this low-rank component again from incomplete data. Several synthetic experiments show the advantages of incorporating information about the corruption into the algorithm. Further experiments on image data confirm the importance of considering a low-rank component in the data and show that the algorithm compares favourably to its closest dictionary learning counterparts, wKSVD and BPFA, either in terms of computational complexity or in terms of consistency between the dictionaries learned from corrupted and uncorrupted data...
2018: EURASIP Journal on Advances in Signal Processing
Shijie Zhao, Junwei Han, Xi Jiang, Heng Huang, Huan Liu, Jinglei Lv, Lei Guo, Tianming Liu
In recent years, natural stimuli such as audio excerpts or video streams have received increasing attention in neuroimaging studies. Compared with conventional simple, idealized and repeated artificial stimuli, natural stimuli contain more unrepeated, dynamic and complex information that are more close to real-life. However, there is no direct correspondence between the stimuli and any sensory or cognitive functions of the brain, which makes it difficult to apply traditional hypothesis-driven analysis methods (e...
February 27, 2018: Neuroinformatics
Marcelo V W Zibetti, Azadeh Sharafi, Ricardo Otazo, Ravinder R Regatte
PURPOSE: To evaluate the feasibility of using compressed sensing (CS) to accelerate 3D-T1ρ mapping of cartilage and to reduce total scan times without degrading the estimation of T1ρ relaxation times. METHODS: Fully sampled 3D-T1ρ datasets were retrospectively undersampled by factors 2-10. CS reconstruction using 12 different sparsifying transforms were compared, including finite differences, temporal and spatial wavelets, learned transforms using principal component analysis (PCA) and K-means singular value decomposition (K-SVD), explicit exponential models, low rank and low rank plus sparse models...
February 25, 2018: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
Shang Zhang, Yuhan Dong, Hongyan Fu, Shao-Lun Huang, Lin Zhang
The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning...
February 22, 2018: Sensors
He Zhang, Vishal M Patel
We propose novel convolutional sparse and low-rank coding-based methods for cartoon and texture decomposition. In our method, we first learn a set of generic filters that can efficiently represent cartoon-and texture-type images. Then, using these learned filters, we propose two optimization frameworks to decompose a given image into cartoon and texture components: convolutional sparse coding-based image decomposition; and convolutional low-rank coding-based image decomposition. By working directly on the whole image, the proposed image separation algorithms do not need to divide the image into overlapping patches for leaning local dictionaries...
May 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Wujie Zhou, Lu Yu, Yang Zhou, Weiwei Qiu, Ming-Wei Wu, Ting Luo
The blind quality evaluation of screen content images (SCIs) and natural scene images (NSIs) has become an important, yet very challenging issue. In this paper, we present an effective blind quality evaluation technique for SCIs and NSIs based on a dictionary of learned local and global quality features. First, a local dictionary is constructed using local normalized image patches and conventional -means clustering. With this local dictionary, the learned local quality features can be obtained using a locality-constrained linear coding with max pooling...
May 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Tanja Platt, Reiner Umathum, Thomas M Fiedler, Armin M Nagel, Andreas K Bitz, Florian Maier, Peter Bachert, Mark E Ladd, Mark O Wielpütz, Hans-Ulrich Kauczor, Nicolas G R Behl
PURPOSE: This work faces three challenges of sodium (23 Na) torso MRI on the way to quantitative 23 Na MRI: Development of a 23 Na radiofrequency transmit and receive coil covering a large part of the human body in width and length for 23 Na MRI at 7 T; reduction of blurring due to respiration in free-breathing 23 Na MRI using a self-gating approach; and reduction of image noise using a compressed-sensing reconstruction. METHODS: An oval-shaped birdcage resonator with a large field of view of (400 mm)3 and a homogeneous transmit and receive field distribution was designed, simulated, and implemented on a 7T MR system...
February 9, 2018: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
Stephanie Kovalchik, Machar Reid
Shots are an essential part of the language of tennis yet little is known about the distinct types of shots in the professional game. In this study, we build a taxonomy of shots for elite tennis players using tracking data from multiple years of men's and women's matches at the Australian Open. Our taxonomy is constructed using model-based multi-stage functional data clustering, an unsupervised machine learning approach. Among 270,023 men's and 178,136 women's shots, we found 13 distinct types of serves to both the Ad and Deuce court for male players and 17 and 15 types to the Ad and Deuce for female players...
February 8, 2018: Journal of Sports Sciences
Saiprasad Ravishankar, Raj Rao Nadakuditi, Jeffrey A Fessler
The sparsity of signals in a transform domain or dictionary has been exploited in applications such as compression, denoising and inverse problems. More recently, data-driven adaptation of synthesis dictionaries has shown promise compared to analytical dictionary models. However, dictionary learning problems are typically non-convex and NP-hard, and the usual alternating minimization approaches for these problems are often computationally expensive, with the computations dominated by the NP-hard synthesis sparse coding step...
December 2017: IEEE Transactions on Computational Imaging
Sushanth Govinahallisathyanarayana, Bo Ning, Rui Cao, Song Hu, John A Hossack
Photoacoustic microscopy (PAM) capitalizes on the optical absorption of blood hemoglobin to enable label-free high-contrast imaging of the cerebral microvasculature in vivo. Although time-resolved ultrasonic detection equips PAM with depth-sectioning capability, most of the data at depths are often obscured by acoustic reverberant artifacts from superficial cortical layers and thus unusable. In this paper, we present a first-of-a-kind dictionary learning algorithm to remove the reverberant signal while preserving underlying microvascular anatomy...
January 17, 2018: Scientific Reports
Yanhua Qin, Yumin Liu, Jianyi Liu, Zhongyuan Yu
Sparse Bayesian learning (SBL) is applied to the coprime array for underdetermined wideband direction of arrival (DOA) estimation. Using the augmented covariance matrix, the coprime array can achieve a higher number of degrees of freedom (DOFs) to resolve more sources than the number of physical sensors. The sparse-based DOA estimation can deteriorate the detection and estimation performance because the sources may be off the search grid no matter how fine the grid is. This dictionary mismatch problem can be well resolved by the SBL using fixed point updates...
January 16, 2018: Sensors
Manuel Lobo, Andre Lamurias, Francisco M Couto
Named-Entity Recognition is commonly used to identify biological entities such as proteins, genes, and chemical compounds found in scientific articles. The Human Phenotype Ontology (HPO) is an ontology that provides a standardized vocabulary for phenotypic abnormalities found in human diseases. This article presents the Identifying Human Phenotypes (IHP) system, tuned to recognize HPO entities in unstructured text. IHP uses Stanford CoreNLP for text processing and applies Conditional Random Fields trained with a rich feature set, which includes linguistic, orthographic, morphologic, lexical, and context features created for the machine learning-based classifier...
2017: BioMed Research International
Wiktor Młynarski, Josh H McDermott
Interaction with the world requires an organism to transform sensory signals into representations in which behaviorally meaningful properties of the environment are made explicit. These representations are derived through cascades of neuronal processing stages in which neurons at each stage recode the output of preceding stages. Explanations of sensory coding may thus involve understanding how low-level patterns are combined into more complex structures. To gain insight into such midlevel representations for sound, we designed a hierarchical generative model of natural sounds that learns combinations of spectrotemporal features from natural stimulus statistics...
March 2018: Neural Computation
Ming Shi, Weiming Shen, Yanwen Chong, Hong-Qiang Wang
Inferring gene regulatory networks (GRNs) from gene expression data is an important but challenging issue in systems biology. Here, the authors propose a dictionary learning-based approach that aims to infer GRNs by globally mining regulatory signals, known or latent. Gene expression is often regulated by various regulatory factors, some of which are observed and some of which are latent. The authors assume that all regulators are unknown for a target gene and the expression of the target gene can be mapped into a regulatory space spanned by all the regulators...
December 2017: IET Systems Biology
Marco Basaldella, Lenz Furrer, Carlo Tasso, Fabio Rinaldi
BACKGROUND: This article describes a high-recall, high-precision approach for the extraction of biomedical entities from scientific articles. METHOD: The approach uses a two-stage pipeline, combining a dictionary-based entity recognizer with a machine-learning classifier. First, the OGER entity recognizer, which has a bias towards high recall, annotates the terms that appear in selected domain ontologies. Subsequently, the Distiller framework uses this information as a feature for a machine learning algorithm to select the relevant entities only...
November 9, 2017: Journal of Biomedical Semantics
Chin Lin, Chia-Jung Hsu, Yu-Sheng Lou, Shih-Jen Yeh, Chia-Cheng Lee, Sui-Lung Su, Hsiang-Cheng Chen
BACKGROUND: Automated disease code classification using free-text medical information is important for public health surveillance. However, traditional natural language processing (NLP) pipelines are limited, so we propose a method combining word embedding with a convolutional neural network (CNN). OBJECTIVE: Our objective was to compare the performance of traditional pipelines (NLP plus supervised machine learning models) with that of word embedding combined with a CNN in conducting a classification task identifying International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes in discharge notes...
November 6, 2017: Journal of Medical Internet Research
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