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Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning

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JOURNAL OF BIOMEDICAL OPTICS 2017年第1期22卷 016012-016012页

作者： Sun, Yankui Li, Shan Sun, Zhongyang Tsinghua Univ Dept Comp Sci & Technol 30 Shuangqing Rd Beijing 100084 Peoples R China Beihang Univ Sch Software 37 Xueyuan Rd Beijing 100191 Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Higher Educ Mega Ctr Univ Town 132 East Waihuan Rd Guangzhou 510006 Guangdong Peoples R China

We propose a framework for automated detection of dry age-related macular degeneration (AMD) and diabetic macular edema (DME) from retina optical coherence tomography (OCT) images, based on sparse coding and dictionary learning. The study aims to improve the classification performance of state-of-the-art methods. First, our method presents a general approach to automatically align and crop retina regions;then it obtains global representations of images by using sparse coding and a spatial pyramid;finally, a multiclass linear support vector machine classifier is employed for classification. We apply two datasets for validating our algorithm: Duke spectral domain OCT (SD-OCT) dataset, consisting of volumetric scans acquired from 45 subjects-15 normal subjects, 15 AMD patients, and 15 DME patients;and clinical SD-OCT dataset, consisting of 678 OCT retina scans acquired from clinics in Beijing-168, 297, and 213 OCT images for AMD, DME, and normal retinas, respectively. For the former dataset, our classifier correctly identifies 100%, 100%, and 93.33% of the volumes with DME, AMD, and normal subjects, respectively, and thus performs much better than the conventional method;for the latter dataset, our classifier leads to a correct classification rate of 99.67%, 99.67%, and 100.00% for DME, AMD, and normal images, respectively. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： optical coherence tomography age-related macular degeneration diabetic macular edema sparse coding max pooling spatial pyramid matching

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HUMAN ACTION RECOGNITION USING A NEW sparse coding APPROACH

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BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2017年第2期29卷

作者： Nikpour, Mohsen Mollaei, Mohammad Reza Karami Ghaderi, Reza Babol Univ Technol Dept Comp & Elect Engn Babol Sar Iran Shahid Beheshti Univ Technol Dept Nucl Engn Tehran Iran

Human action recognition (HAR) is a challenging problem because of the complexity and similarity in different actions. In recent years, many methods have been proposed for HAR. sparse coding-based approaches have been widely used in this field. Also, many works have been done based on manifold learning theory. When the videos are similar but from different classes, their sparse codes may be similar and the actions might be classified mistakenly. In this paper, a multi-modal affine graph regularized sparse coding approach is proposed for solving this problem in HAR. At first, HOG3D, HOG/Hof and SURF3D descriptors were extracted from the action datasets, then the sparse codes have been obtained for each descriptor using the proposed method. The dictionary learning method used in this step has more discrimination power in respect to the traditional methods. Then, these codes are scored differently using SVM classifier and at last a Naive Bayes leads to a final decision. Experiments on KTH, Weizmann and UCF Sport action datasets show that the proposed method can significantly outperform several previous methods in human action classification especially in real-world data.

关键词： sparse coding Manifold learning Affinity constraint Naive Bayes classifier

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Learning Efficient Data Representations With Orthogonal sparse coding

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2016年第3期2卷 177-189页

作者： Schuetze, Henry Barth, Erhardt Martinetz, Thomas Univ Lubeck Inst Neuro & Bioinformat D-23562 Lubeck Germany

We present the learning algorithm orthogonal sparse coding (OSC) to find an orthogonal basis in which a given data set has a maximally sparse representation. OSC is based on stochastic descent by Hebbian-like updates and Gram-Schmidt orthogonalizations, and is motivated by an algorithm that we introduce as the canonical approach (CA). First, we evaluate how well OSC can recover a generating basis from synthetic data. We show that, in contrast to competing methods, OSC can recover the generating basis for quite low and, remarkably, unknown sparsity levels. Moreover, on natural image patches and on images of handwritten digits, OSC learns orthogonal bases that attain significantly sparser representations compared to alternative orthogonal transforms. Furthermore, we demonstrate an application of OSC for image compression by showing that the rate-distortion performance can be improved relative to the JPEG standard. Finally, we demonstrate the state-of-the-art image denoising performance of OSC dictionaries. Our results demonstrate the potential of OSC for feature extraction, data compression, and image denoising, which is due to two important aspects: 1) the learned bases are adapted to the signal class, and 2) the sparse approximation problem can be solved efficiently and exactly.

关键词： Blind source separation dictionary learning image compression image denoising orthogonal mixture sparse coding sparse component analysis sparse representation transform coding

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Efficient Local Feature Encoding for Human Action Recognition with Approximate sparse coding

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2016年第4期E99D卷 1212-1220页

作者： Wang, Yu Kato, Jien Nagoya Univ Grad Sch Informat Sci Nagoya Aichi 4648603 Japan

Local spatio-temporal features are popular in the human action recognition task. In practice, they are usually coupled with a feature encoding approach, which helps to obtain the video-level vector representations that can be used in learning and recognition. In this paper, we present an efficient local feature encoding approach, which is called Approximate sparse coding (ASC). ASC computes the sparse codes for a large collection of prototype local feature descriptors in the off-line learning phase using sparse coding (SC) and look up the nearest prototype's precomputed sparse code for each to-be-encoded local feature in the encoding phase using Approximate Nearest Neighbour (ANN) search. It shares the low dimensionality of SC and the high speed of ANN, which are both desired properties for a local feature encoding approach. ASC has been excessively evaluated on the KTH dataset and the HMDB51 dataset. We confirmed that it is able to encode large quantity of local video features into discriminative low dimensional representations efficiently.

关键词： approximate sparse coding sparse coding approximate nearest neighbour local feature encoding action recognition

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Optimization of learned dictionary for sparse coding in speech processing

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NEUROCOMPUTING 2016年第Part3期173卷 471-482页

作者： He, Yongjun Sun, Guanglu Han, Jiqing Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150080 Peoples R China Harbin Inst Technol Harbin 150001 Peoples R China

As a promising technique, sparse coding has been widely used for the analysis, representation, compression, denoising and separation of speech. This technique needs a good dictionary which contains atoms to represent speech signals. Although many methods have been proposed to learn such a dictionary, there are still two problems. First, unimportant atoms bring a heavy computational load to sparse decomposition and reconstruction, which prevents sparse coding from real-time application. Second, in speech denoising and separation, harmful atoms have no or ignorable contributions to reducing the sparsity degree but increase the source confusion, resulting in severe distortions. To solve these two problems, we first analyze the inherent assumptions of sparse coding and show that distortion can be caused if the assumptions do not hold true. Next, we propose two methods to optimize a given dictionary by removing unimportant atoms and harmful atoms, respectively. Experiments show that the proposed methods can further improve the performance of dictionaries. (C) 2015 Elsevier B.V. All rights reserved.

关键词： sparse coding Speech denoising Speech recognition Dictionary optimization

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p-Laplacian Regularized sparse coding for Human Activity Recognition

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2016年第8期63卷 5120-5129页

作者： Liu, Weifeng Zha, Zheng-Jun Wang, Yanjiang Lu, Ke Tao, Dacheng China Univ Petr East China Coll Informat & Control Engn Qingdao 266580 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230026 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Technol Sydney Ctr Quantum Computat & Intelligent Syst Ultimo NSW 2007 Australia Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia

Human activity analysis in videos has increasingly attracted attention in computer vision research with the massive number of videos now accessible online. Although many recognition algorithms have been reported recently, activity representation is challenging. Recently, manifold regularized sparse coding has obtained promising performance in action recognition, because it simultaneously learns the sparse representation and preserves the manifold structure. In this paper, we propose a generalized version of Laplacian regularized sparse coding for human activity recognition called p-Laplacian regularized sparse coding (pLSC). The proposed method exploits p-Laplacian regularization to preserve the local geometry. The p-Laplacian is a nonlinear generalization of standard graph Laplacian and has tighter isoperimetric inequality. As a result, pLSC provides superior theoretical evidence than standard Laplacian regularized sparse coding with a proper p. We also provide a fast iterative shrinkage-thresholding algorithm for the optimization of pLSC. Finally, we input the sparse codes learned by the pLSC algorithm into support vector machines and conduct extensive experiments on the unstructured social activity attribute dataset and human motion database (HMDB51) for human activity recognition. The experimental results demonstrate that the proposed pLSC algorithm outperforms the manifold regularized sparse coding algorithms including the standard Laplacian regularized sparse coding algorithm with a proper p.

关键词： Human activity recognition manifold p-Laplacian sparse coding

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Medical image registration using sparse coding of image patches

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COMPUTERS IN BIOLOGY AND MEDICINE 2016年 73卷 56-70页

作者： Afzali, Maryam Ghaffari, Aboozar Fatemizadeh, Emad Soltanian-Zadeh, Hamid Sharif Univ Technol Biomed Signal & Image Proc Lab BiSIPL Dept Elect Engn Tehran Iran Univ Tehran Sch Elect & Comp Engn CIPCE Tehran Iran Inst Res Fundamental Sci IPM Sch Cognit Sci Tehran Iran Henry Ford Hlth Syst Dept Radiol Image Anal Lab Detroit MI USA Henry Ford Hlth Syst Dept Res Adm Image Anal Lab Detroit MI USA

Image registration is a basic task in medical image processing applications like group analysis and atlas construction. Similarity measure is a critical ingredient of image registration. Intensity distortion of medical images is not considered in most previous similarity measures. Therefore, in the presence of bias field distortions, they do not generate an acceptable registration. In this paper, we propose a sparse based similarity measure for mono-modal images that considers non-stationary intensity and spatially-varying distortions: The main idea behind this measure is that the aligned image is constructed by an analysis dictionary trained using the image patches. For this purpose, we use "Analysis K-SVD" to train the dictionary and find the sparse coefficients. We utilize image patches to construct the analysis dictionary and then we employ the proposed sparse similarity measure to find a non-rigid transformation using free form deformation (FFD). Experimental results show that the proposed approach is able to robustly register 2D and 3D images in both simulated and real cases. The proposed method outperforms other state-of-the-art similarity measures and decreases the transformation error compared to the previous methods. Even in the presence of bias field distortion, the proposed method aligns images without any preprocessing. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Non-rigid image registration Analysis K-SVD algorithm sparse coding

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sparse coding approaches for neuron spike sorting

Sparse coding approaches for neuron spike sorting

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2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Kai Yang Haifeng Wu Yu Zeng School of Electrical and Information Technology Yunnan Minzu University Key Laboratory of Wireless Sensor Networks of Yunnan Province

ISBN: (纸本)9781509001668

Spike sorting is in fact clustering analysis,which will face a problem in high dimensions: the "curse of dimensionality".This paper proposes a new dimension-reduction approach for sorting spikes from multiunit *** new approach applies the principle of sparse coding to analyzing high-dimensional spike data within the frameworks of clustering,classification and dimension ***,the sparse representation adopts fast parallel active-set optimization to speed up the convergence of the *** our experiment,we compare the proposed approach with traditional *** is shown that our approach has fewer extracted features and better classification accuracy especially in similar spike waveforms.

关键词： Spike sorting Principal Components Analysis sparse coding

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Orthogonal Projective sparse coding for image representation

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NEUROCOMPUTING 2016年第Part2期173卷 270-277页

作者： Zhao, Wei Liu, Zheng Guan, Ziyu Lin, Binbin Cai, Deng Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China Northwest Univ China Coll Informat & Technol Xian 710127 Shaanxi Peoples R China Zhejiang Univ State Key Lab CAD & CG Hangzhou 310058 Zhejiang Peoples R China

We consider the problem of image representation for visual analysis. When representing images as vectors, the feature space is of very high dimensionality, which makes it difficult for applying statistical techniques for visual analysis. One then hope to apply matrix factorization techniques, such as Singular Vector Decomposition (SVD) to learn the low dimensional hidden concept space. Among various matrix factorization techniques, sparse coding receives considerable interests in recent years because its sparse representation leads to an elegant interpretation. However, most of the existing sparse coding algorithms are computational expensive since they compute the basis vectors and the representations iteratively. In this paper, we propose a novel method, called Orthogonal Projective sparse coding (OPSC), for efficient and effective image representation and analysis. Integrating the techniques from manifold learning and sparse coding, OPSC provides a sparse representation which can capture the intrinsic geometric structure of the image space. Extensive experimental results on real world applications demonstrate the effectiveness and efficiency of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Manifold sparse coding Orthogonal projection

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Accurate HEp-2 cell classification based on sparse coding of Superpixels

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PATTERN RECOGNITION LETTERS 2016年第Part1期82卷 64-71页

作者： Ensafi, Shahab Lu, Shijian Kassim, Ashraf A. Tan, Chew Lim Natl Univ Singapore Elect & Comp Engn Fac Engn 4 Engn Dr 3Block E45-45 Singapore 117583 Singapore ASTAR Inst Infocomm Res 1 Fusionopolis Way21-01 ConnexisSouth Tower Singapore 138632 Singapore Natl Univ Singapore Sch Comp Dept Comp Sci Comp 1 13 Comp Dr Singapore 117417 Singapore

Autoimmune Diseases (AD) are among the top 10 leading causes of death in female children and women in all age groups up to 64 years. They are widely diagnosed by various antibody tests that typically apply the Indirect Immunofluorescence (IIF) to the Human Epithelial Type-2 (HEp-2) cells. Automated classification of Hep-2 cells has attracted much research interest in recent years, and many of these approaches employ patch-based models and the Bag of Words (BoW) scheme, but often face several typical constraints such as the need to process a huge number of overlapped image patches, tuning of various parameters and etc. We propose a superpixel based Hep-2 cell classification technique by calculating the sparse codes of image patches which are prepared in a more intelligent way. In particular, the super-pixel approach guides the determination of the right image patches while aggregating the neighboring pixels of similar patterns. In addition, we introduce "extended superpixels" which is able to capture the most discriminative gradient information across the boundary of the HEp-2 cell images. The proposed technique has been evaluated over two public datasets (ICPR2012 and ICIP2013) and experiments show superior performance in both classification accuracy and speed of model training and cell classification. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Autoimmune diseases HEp-2 cells Computer-aided diagnosis Cell classification Superpixel sparse coding

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