1
|
He J, Pertsov AM, Cherry EM, Fenton FH, Roney CH, Niederer SA, Zang Z, Mangharam R. Fiber Organization has Little Effect on Electrical Activation Patterns during Focal Arrhythmias in the Left Atrium. ARXIV 2023:arXiv:2210.16497v3. [PMID: 36776816 PMCID: PMC9915751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
Over the past two decades there has been a steady trend towards the development of realistic models of cardiac conduction with increasing levels of detail. However, making models more realistic complicates their personalization and use in clinical practice due to limited availability of tissue and cellular scale data. One such limitation is obtaining information about myocardial fiber organization in the clinical setting. In this study, we investigated a chimeric model of the left atrium utilizing clinically derived patient-specific atrial geometry and a realistic, yet foreign for a given patient fiber organization. We discovered that even significant variability of fiber organization had a relatively small effect on the spatio-temporal activation pattern during regular pacing. For a given pacing site, the activation maps were very similar across all fiber organizations tested.
Collapse
Affiliation(s)
- Jiyue He
- Department of Electrical and Systems Engineering, University of Pennsylvania, USA
| | | | - Elizabeth M Cherry
- School of Computational Science and Engineering, Georgia Institute of Technology, USA
| | | | - Caroline H Roney
- School of Engineering and Materials Science, Queen Mary University of London, UK
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Zirui Zang
- Department of Electrical and Systems Engineering, University of Pennsylvania, USA
| | - Rahul Mangharam
- Department of Electrical and Systems Engineering, University of Pennsylvania, USA
| |
Collapse
|
2
|
Seong D, Lee E, Kim Y, Han S, Lee J, Jeon M, Kim J. Three-dimensional reconstructing undersampled photoacoustic microscopy images using deep learning. PHOTOACOUSTICS 2023; 29:100429. [PMID: 36544533 PMCID: PMC9761854 DOI: 10.1016/j.pacs.2022.100429] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/29/2022] [Accepted: 11/28/2022] [Indexed: 05/31/2023]
Abstract
Spatial sampling density and data size are important determinants of the imaging speed of photoacoustic microscopy (PAM). Therefore, undersampling methods that reduce the number of scanning points are typically adopted to enhance the imaging speed of PAM by increasing the scanning step size. Since undersampling methods sacrifice spatial sampling density, by considering the number of data points, data size, and the characteristics of PAM that provides three-dimensional (3D) volume data, in this study, we newly reported deep learning-based fully reconstructing the undersampled 3D PAM data. The results of quantitative analyses demonstrate that the proposed method exhibits robustness and outperforms interpolation-based reconstruction methods at various undersampling ratios, enhancing the PAM system performance with 80-times faster-imaging speed and 800-times lower data size. The proposed method is demonstrated to be the closest model that can be used under experimental conditions, effectively shortening the imaging time with significantly reduced data size for processing.
Collapse
Affiliation(s)
- Daewoon Seong
- School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Euimin Lee
- School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Yoonseok Kim
- School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Sangyeob Han
- School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
- Institute of Biomedical Engineering, School of Medicine, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Jaeyul Lee
- Department of Bioengineering, University of California, Los Angeles, CA 90095, USA
| | - Mansik Jeon
- School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Jeehyun Kim
- School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| |
Collapse
|
3
|
Eyiokur FI, Kantarcı A, Erakın ME, Damer N, Ofli F, Imran M, Križaj J, Salah AA, Waibel A, Štruc V, Ekenel HK. A survey on computer vision based human analysis in the COVID-19 era. IMAGE AND VISION COMPUTING 2023; 130:104610. [PMID: 36540857 PMCID: PMC9755265 DOI: 10.1016/j.imavis.2022.104610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 12/11/2022] [Indexed: 06/17/2023]
Abstract
The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of ( i ) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and ( ii ) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for computer vision researchers but also the general public.
Collapse
Affiliation(s)
- Fevziye Irem Eyiokur
- Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Alperen Kantarcı
- Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey
| | - Mustafa Ekrem Erakın
- Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey
| | - Naser Damer
- Fraunhofer Institute for Computer Graphics Research IGD, Darmstadt, Germany
- Department of Computer Science, TU Darmstadt, Darmstadt, Germany
| | - Ferda Ofli
- Qatar Computing Research Institute, HBKU, Doha, Qatar
| | | | - Janez Križaj
- Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, 1000 Ljubljana, Slovenia
| | - Albert Ali Salah
- Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
- Department of Computer Engineering, Bogˇaziçi University, Istanbul, Turkey
| | - Alexander Waibel
- Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany
- Carnegie Mellon University, Pittsburgh, United States
| | - Vitomir Štruc
- Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, 1000 Ljubljana, Slovenia
| | - Hazım Kemal Ekenel
- Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey
| |
Collapse
|
4
|
Xu C, Ma D, Ding Q, Zhou Y, Zheng H. PlantPhoneDB: A manually curated pan-plant database of ligand-receptor pairs infers cell-cell communication. PLANT BIOTECHNOLOGY JOURNAL 2022; 20:2123-2134. [PMID: 35842742 PMCID: PMC9616517 DOI: 10.1111/pbi.13893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 07/10/2022] [Accepted: 07/13/2022] [Indexed: 06/15/2023]
Abstract
Ligand-receptor pairs play important roles in cell-cell communication for multicellular organisms in response to environmental cues. Recently, the emergence of single-cell RNA-sequencing (scRNA-seq) provides unprecedented opportunities to investigate cellular communication based on ligand-receptor expression. However, so far, no reliable ligand-receptor interaction database is available for plant species. In this study, we developed PlantPhoneDB (https://jasonxu.shinyapps.io/PlantPhoneDB/), a pan-plant database comprising a large number of high-confidence ligand-receptor pairs manually curated from seven resources. Also, we developed a PlantPhoneDB R package, which not only provided optional four scoring approaches that calculate interaction scores of ligand-receptor pairs between cell types but also provided visualization functions to present analysis results. At the PlantPhoneDB web interface, the processed datasets and results can be searched, browsed, and downloaded. To uncover novel cell-cell communication events in plants, we applied the PlantPhoneDB R package on GSE121619 dataset to infer significant cell-cell interactions of heat-shocked root cells in Arabidopsis thaliana. As a result, the PlantPhoneDB predicted the actively communicating AT1G28290-AT2G14890 ligand-receptor pair in atrichoblast-cortex cell pair in Arabidopsis thaliana. Importantly, the downstream target genes of this ligand-receptor pair were significantly enriched in the ribosome pathway, which facilitated plants adapting to environmental changes. In conclusion, PlantPhoneDB provided researchers with integrated resources to infer cell-cell communication from scRNA-seq datasets.
Collapse
Affiliation(s)
- Chaoqun Xu
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and EcologyXiamen UniversityXiamenChina
| | - Dongna Ma
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and EcologyXiamen UniversityXiamenChina
| | - Qiansu Ding
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and EcologyXiamen UniversityXiamenChina
| | - Ying Zhou
- National Institute for Data Science in Health and Medicine, School of MedicineXiamen UniversityXiamenChina
| | - Hai‐Lei Zheng
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and EcologyXiamen UniversityXiamenChina
| |
Collapse
|
5
|
Metasurface-driven full-space structured light for three-dimensional imaging. Nat Commun 2022; 13:5920. [PMID: 36216802 PMCID: PMC9550774 DOI: 10.1038/s41467-022-32117-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 07/18/2022] [Indexed: 11/08/2022] Open
Abstract
Structured light (SL)-based depth-sensing technology illuminates the objects with an array of dots, and backscattered light is monitored to extract three-dimensional information. Conventionally, diffractive optical elements have been used to form laser dot array, however, the field-of-view (FOV) and diffraction efficiency are limited due to their micron-scale pixel size. Here, we propose a metasurface-enhanced SL-based depth-sensing platform that scatters high-density ~10 K dot array over the 180° FOV by manipulating light at subwavelength-scale. As a proof-of-concept, we place face masks one on the beam axis and the other 50° apart from axis within distance of 1 m and estimate the depth information using a stereo matching algorithm. Furthermore, we demonstrate the replication of the metasurface using the nanoparticle-embedded-resin (nano-PER) imprinting method which enables high-throughput manufacturing of the metasurfaces on any arbitrary substrates. Such a full-space diffractive metasurface may afford ultra-compact depth perception platform for face recognition and automotive robot vision applications. 3D depth sensing with structured light enables simultaneous imaging of multiple objects, but has limited field of view and low efficiency. Here, the authors demonstrate 3D imaging with scattered light from a metasurface composed of periodic supercells, covering a 180° field of view with a high-density dot array.
Collapse
|
6
|
Li W, Yang Z, Lv J, Zheng T, Li M, Sun C. Detection of Small-Sized Insects in Sticky Trapping Images Using Spectral Residual Model and Machine Learning. FRONTIERS IN PLANT SCIENCE 2022; 13:915543. [PMID: 35837447 PMCID: PMC9274131 DOI: 10.3389/fpls.2022.915543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 05/24/2022] [Indexed: 06/15/2023]
Abstract
One fundamental component of Integrated pest management (IPM) is field monitoring and growers use information gathered from scouting to make an appropriate control tactics. Whitefly (Bemisia tabaci) and thrips (Frankliniella occidentalis) are two most prominent pests in greenhouses of northern China. Traditionally, growers estimate the population of these pests by counting insects caught on sticky traps, which is not only a challenging task but also an extremely time-consuming one. To alleviate this situation, this study proposed an automated detection approach to meet the need for continuous monitoring of pests in greenhouse conditions. Candidate targets were firstly located using a spectral residual model and then different color features were extracted. Ultimately, Whitefly and thrips were identified using a support vector machine classifier with an accuracy of 93.9 and 89.9%, a true positive rate of 93.1 and 80.1%, and a false positive rate of 9.9 and 12.3%, respectively. Identification performance was further tested via comparison between manual and automatic counting with a coefficient of determination, R 2, of 0.9785 and 0.9582. The results show that the proposed method can provide a comparable performance with previous handcrafted feature-based methods, furthermore, it does not require the support of high-performance hardware compare with deep learning-based method. This study demonstrates the potential of developing a vision-based identification system to facilitate rapid gathering of information pertaining to numbers of small-sized pests in greenhouse agriculture and make a reliable estimation of overall population density.
Collapse
Affiliation(s)
- Wenyong Li
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Zhankui Yang
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- College of Computer Science and Technology, Beijing University of Technology, Beijing, China
| | - Jiawei Lv
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Tengfei Zheng
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- College of Information, Shanghai Ocean University, Shanghai, China
| | - Ming Li
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Chuanheng Sun
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| |
Collapse
|
7
|
Huang L, Luo R, Liu X, Hao X. Spectral imaging with deep learning. LIGHT, SCIENCE & APPLICATIONS 2022; 11:61. [PMID: 35296633 PMCID: PMC8927154 DOI: 10.1038/s41377-022-00743-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 01/30/2022] [Accepted: 02/15/2022] [Indexed: 05/19/2023]
Abstract
The goal of spectral imaging is to capture the spectral signature of a target. Traditional scanning method for spectral imaging suffers from large system volume and low image acquisition speed for large scenes. In contrast, computational spectral imaging methods have resorted to computation power for reduced system volume, but still endure long computation time for iterative spectral reconstructions. Recently, deep learning techniques are introduced into computational spectral imaging, witnessing fast reconstruction speed, great reconstruction quality, and the potential to drastically reduce the system volume. In this article, we review state-of-the-art deep-learning-empowered computational spectral imaging methods. They are further divided into amplitude-coded, phase-coded, and wavelength-coded methods, based on different light properties used for encoding. To boost future researches, we've also organized publicly available spectral datasets.
Collapse
Affiliation(s)
- Longqian Huang
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Technology, Zhejiang University, Hangzhou, 310027, China
| | - Ruichen Luo
- College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China
| | - Xu Liu
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Technology, Zhejiang University, Hangzhou, 310027, China
| | - Xiang Hao
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Technology, Zhejiang University, Hangzhou, 310027, China.
- Jiaxing Key Laboratory of Photonic Sensing & Intelligent Imaging, Jiaxing, 314000, China.
- Intelligent Optics & Photonics Research Center, Jiaxing Research Institute Zhejiang University, Jiaxing, 314000, China.
| |
Collapse
|
8
|
Chen Y, Zhang H, Wang Y, Yang Y, Zhou X, Wu QMJ. MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1032-1041. [PMID: 33326377 PMCID: PMC8544938 DOI: 10.1109/tmi.2020.3045295] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Anomaly detection refers to the identification of cases that do not conform to the expected pattern, which takes a key role in diverse research areas and application domains. Most of existing methods can be summarized as anomaly object detection-based and reconstruction error-based techniques. However, due to the bottleneck of defining encompasses of real-world high-diversity outliers and inaccessible inference process, individually, most of them have not derived groundbreaking progress. To deal with those imperfectness, and motivated by memory-based decision-making and visual attention mechanism as a filter to select environmental information in human vision perceptual system, in this paper, we propose a Multi-scale Attention Memory with hash addressing Autoencoder network (MAMA Net) for anomaly detection. First, to overcome a battery of problems result from the restricted stationary receptive field of convolution operator, we coin the multi-scale global spatial attention block which can be straightforwardly plugged into any networks as sampling, upsampling and downsampling function. On account of its efficient features representation ability, networks can achieve competitive results with only several level blocks. Second, it's observed that traditional autoencoder can only learn an ambiguous model that also reconstructs anomalies "well" due to lack of constraints in training and inference process. To mitigate this challenge, we design a hash addressing memory module that proves abnormalities to produce higher reconstruction error for classification. In addition, we couple the mean square error (MSE) with Wasserstein loss to improve the encoding data distribution. Experiments on various datasets, including two different COVID-19 datasets and one brain MRI (RIDER) dataset prove the robustness and excellent generalization of the proposed MAMA Net.
Collapse
|
9
|
Zhou T, Fan DP, Cheng MM, Shen J, Shao L. RGB-D salient object detection: A survey. COMPUTATIONAL VISUAL MEDIA 2021; 7:37-69. [PMID: 33432275 PMCID: PMC7788385 DOI: 10.1007/s41095-020-0199-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 10/07/2020] [Indexed: 06/12/2023]
Abstract
Salient object detection, which simulates human visual perception in locating the most significant object(s) in a scene, has been widely applied to various computer vision tasks. Now, the advent of depth sensors means that depth maps can easily be captured; this additional spatial information can boost the performance of salient object detection. Although various RGB-D based salient object detection models with promising performance have been proposed over the past several years, an in-depth understanding of these models and the challenges in this field remains lacking. In this paper, we provide a comprehensive survey of RGB-D based salient object detection models from various perspectives, and review related benchmark datasets in detail. Further, as light fields can also provide depth maps, we review salient object detection models and popular benchmark datasets from this domain too. Moreover, to investigate the ability of existing models to detect salient objects, we have carried out a comprehensive attribute-based evaluation of several representative RGB-D based salient object detection models. Finally, we discuss several challenges and open directions of RGB-D based salient object detection for future research. All collected models, benchmark datasets, datasets constructed for attribute-based evaluation, and related code are publicly available at https://github.com/taozh2017/RGBD-SODsurvey.
Collapse
Affiliation(s)
- Tao Zhou
- Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, United Arab Emirates
| | - Deng-Ping Fan
- Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, United Arab Emirates
| | | | - Jianbing Shen
- Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, United Arab Emirates
| | - Ling Shao
- Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, United Arab Emirates
| |
Collapse
|
10
|
|
11
|
Mehta R, Kim HJ, Wang S, Johnson SC, Yuan M, Singh V. LOCALIZING DIFFERENTIALLY EVOLVING COVARIANCE STRUCTURES VIA SCAN STATISTICS. QUARTERLY OF APPLIED MATHEMATICS 2018; 77:357-398. [PMID: 35125524 PMCID: PMC8813049 DOI: 10.1090/qam/1522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Recent results in coupled or temporal graphical models offer schemes for estimating the relationship structure between features when the data come from related (but distinct) longitudinal sources. A novel application of these ideas is for analyzing group-level differences, i.e., in identifying if trends of estimated objects (e.g., covariance or precision matrices) are different across disparate conditions (e.g., gender or disease). Often, poor effect sizes make detecting the differential signal over the full set of features difficult: for example, dependencies between only a subset of features may manifest differently across groups. In this work, we first give a parametric model for estimating trends in the space of SPD matrices as a function of one or more covariates. We then generalize scan statistics to graph structures, to search over distinct subsets of features (graph partitions) whose temporal dependency structure may show statistically significant group-wise differences. We theoretically analyze the Family Wise Error Rate (FWER) and bounds on Type 1 and Type 2 error. Evaluating on US census data, we identify groups of states with cultural and legal overlap related to baby name trends and drug usage. On a cohort of individuals with risk factors for Alzheimer's disease (but otherwise cognitively healthy), we find scientifically interesting group differences where the default analysis, i.e., models estimated on the full graph, do not survive reasonable significance thresholds.
Collapse
Affiliation(s)
- Ronak Mehta
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Hyunwoo J Kim
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Shulei Wang
- Department of Statistics, Columbia University, New York, NY 10027, USA
| | - Sterling C Johnson
- Alzheimer's Disease Research Center, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Ming Yuan
- Department of Statistics, Columbia University, New York, NY 10027, USA
| | - Vikas Singh
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
| |
Collapse
|
12
|
Abstract
Motion blur appearing in traffic sign images may lead to poor recognition results, and therefore it is of great significance to study how to deblur the images. In this paper, a novel method for deblurring traffic sign is proposed based on exemplars and several related approaches are also made. First, an exemplar dataset construction method is proposed based on multiple-size partition strategy to lower calculation cost of exemplar matching. Second, a matching criterion based on gradient information and entropy correlation coefficient is also proposed to enhance the matching accuracy. Third, L0.5-norm is introduced as the regularization item to maintain the sparsity of blur kernel. Experiments verify the superiority of the proposed approaches and extensive evaluations against state-of-the-art methods demonstrate the effectiveness of the proposed algorithm.
Collapse
Affiliation(s)
- Houjie Li
- Faculty of Electronic Information & Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China
- College of Information & Communication Engineering, Dalian Minzu University, Dalian, Liaoning, China
| | - Tianshuang Qiu
- Faculty of Electronic Information & Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China
- * E-mail:
| | - Shengyang Luan
- School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, Jiangsu, China
| | - Haiyu Song
- School of Computer Science & Engineering, Dalian Minzu University, Dalian, Liaoning, China
| | - Linxiu Wu
- College of Information & Communication Engineering, Dalian Minzu University, Dalian, Liaoning, China
| |
Collapse
|
13
|
Rangrej SB, Sivaswamy J. Assistive lesion-emphasis system: an assistive system for fundus image readers. J Med Imaging (Bellingham) 2017; 4:024503. [PMID: 28560245 PMCID: PMC5443420 DOI: 10.1117/1.jmi.4.2.024503] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 05/08/2017] [Indexed: 11/14/2022] Open
Abstract
Computer-assisted diagnostic (CAD) tools are of interest as they enable efficient decision-making in clinics and the screening of diseases. The traditional approach to CAD algorithm design focuses on the automated detection of abnormalities independent of the end-user, who can be an image reader or an expert. We propose a reader-centric system design wherein a reader's attention is drawn to abnormal regions in a least-obtrusive yet effective manner, using saliency-based emphasis of abnormalities and without altering the appearance of the background tissues. We present an assistive lesion-emphasis system (ALES) based on the above idea, for fundus image-based diabetic retinopathy diagnosis. Lesion-saliency is learnt using a convolutional neural network (CNN), inspired by the saliency model of Itti and Koch. The CNN is used to fine-tune standard low-level filters and learn high-level filters for deriving a lesion-saliency map, which is then used to perform lesion-emphasis via a spatially variant version of gamma correction. The proposed system has been evaluated on public datasets and benchmarked against other saliency models. It was found to outperform other saliency models by 6% to 30% and boost the contrast-to-noise ratio of lesions by more than 30%. Results of a perceptual study also underscore the effectiveness and, hence, the potential of ALES as an assistive tool for readers.
Collapse
|
14
|
De los Reyes JC, Schönlieb CB, Valkonen T. Bilevel Parameter Learning for Higher-Order Total Variation Regularisation Models. JOURNAL OF MATHEMATICAL IMAGING AND VISION 2017; 57:1-25. [PMID: 32355410 PMCID: PMC7175605 DOI: 10.1007/s10851-016-0662-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Accepted: 05/01/2016] [Indexed: 05/12/2023]
Abstract
We consider a bilevel optimisation approach for parameter learning in higher-order total variation image reconstruction models. Apart from the least squares cost functional, naturally used in bilevel learning, we propose and analyse an alternative cost based on a Huber-regularised TV seminorm. Differentiability properties of the solution operator are verified and a first-order optimality system is derived. Based on the adjoint information, a combined quasi-Newton/semismooth Newton algorithm is proposed for the numerical solution of the bilevel problems. Numerical experiments are carried out to show the suitability of our approach and the improved performance of the new cost functional. Thanks to the bilevel optimisation framework, also a detailed comparison between TGV 2 and ICTV is carried out, showing the advantages and shortcomings of both regularisers, depending on the structure of the processed images and their noise level.
Collapse
Affiliation(s)
- J. C. De los Reyes
- Research Center on Mathematical Modelling (MODEMAT), Escuela Politécnica Nacional, Quito, Ecuador
| | - C.-B. Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - T. Valkonen
- Department of Mathematical Sciences, University of Liverpool, Liverpool, UK
| |
Collapse
|
15
|
Shuang B, Wang W, Shen H, Tauzin LJ, Flatebo C, Chen J, Moringo NA, Bishop LDC, Kelly KF, Landes CF. Generalized recovery algorithm for 3D super-resolution microscopy using rotating point spread functions. Sci Rep 2016; 6:30826. [PMID: 27488312 PMCID: PMC4973222 DOI: 10.1038/srep30826] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Accepted: 07/11/2016] [Indexed: 01/17/2023] Open
Abstract
Super-resolution microscopy with phase masks is a promising technique for 3D imaging and tracking. Due to the complexity of the resultant point spread functions, generalized recovery algorithms are still missing. We introduce a 3D super-resolution recovery algorithm that works for a variety of phase masks generating 3D point spread functions. A fast deconvolution process generates initial guesses, which are further refined by least squares fitting. Overfitting is suppressed using a machine learning determined threshold. Preliminary results on experimental data show that our algorithm can be used to super-localize 3D adsorption events within a porous polymer film and is useful for evaluating potential phase masks. Finally, we demonstrate that parallel computation on graphics processing units can reduce the processing time required for 3D recovery. Simulations reveal that, through desktop parallelization, the ultimate limit of real-time processing is possible. Our program is the first open source recovery program for generalized 3D recovery using rotating point spread functions.
Collapse
Affiliation(s)
- Bo Shuang
- Department of Chemistry, Rice University, Houston, TX 77251, USA
| | - Wenxiao Wang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77251, USA
| | - Hao Shen
- Department of Chemistry, Rice University, Houston, TX 77251, USA
| | | | | | - Jianbo Chen
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77251, USA
| | | | | | - Kevin F. Kelly
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77251, USA
| | - Christy F. Landes
- Department of Chemistry, Rice University, Houston, TX 77251, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77251, USA
| |
Collapse
|
16
|
Zand M, Doraisamy S, Abdul Halin A, Mustaffa MR. Ontology-Based Semantic Image Segmentation Using Mixture Models and Multiple CRFs. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2016; 25:3233-3248. [PMID: 27071174 DOI: 10.1109/tip.2016.2552401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Semantic image segmentation is a fundamental yet challenging problem, which can be viewed as an extension of the conventional object detection with close relation to image segmentation and classification. It aims to partition images into non-overlapping regions that are assigned predefined semantic labels. Most of the existing approaches utilize and integrate low-level local features and high-level contextual cues, which are fed into an inference framework such as, the conditional random field (CRF). However, the lack of meaning in the primitives (i.e., pixels or superpixels) and the cues provides low discriminatory capabilities, since they are rarely object-consistent. Moreover, blind combinations of heterogeneous features and contextual cues exploitation through limited neighborhood relations in the CRFs tend to degrade the labeling performance. This paper proposes an ontology-based semantic image segmentation (OBSIS) approach that jointly models image segmentation and object detection. In particular, a Dirichlet process mixture model transforms the low-level visual space into an intermediate semantic space, which drastically reduces the feature dimensionality. These features are then individually weighed and independently learned within the context, using multiple CRFs. The segmentation of images into object parts is hence reduced to a classification task, where object inference is passed to an ontology model. This model resembles the way by which humans understand the images through the combination of different cues, context models, and rule-based learning of the ontologies. Experimental evaluations using the MSRC-21 and PASCAL VOC'2010 data sets show promising results.
Collapse
|
17
|
Lee TS. The visual system's internal model of the world. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2015; 103:1359-1378. [PMID: 26566294 PMCID: PMC4638327 DOI: 10.1109/jproc.2015.2434601] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
The Bayesian paradigm has provided a useful conceptual theory for understanding perceptual computation in the brain. While the detailed neural mechanisms of Bayesian inference are not fully understood, recent computational and neurophysiological works have illuminated the underlying computational principles and representational architecture. The fundamental insights are that the visual system is organized as a modular hierarchy to encode an internal model of the world, and that perception is realized by statistical inference based on such internal model. In this paper, I will discuss and analyze the varieties of representational schemes of these internal models and how they might be used to perform learning and inference. I will argue for a unified theoretical framework for relating the internal models to the observed neural phenomena and mechanisms in the visual cortex.
Collapse
Affiliation(s)
- Tai Sing Lee
- Professor in the Computer Science Department and the Center for the Neural Basis of Cognition, Carnegie Mellon University, Rm 115, Mellon Institute, 4400 Fifth Avenue, Pittsburgh, PA 15213, U.S.A
| |
Collapse
|
18
|
Ladický L, Russell C, Kohli P, Torr PHS. Associative Hierarchical Random Fields. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2014; 36:1056-1077. [PMID: 26353271 DOI: 10.1109/tpami.2013.165] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
This paper makes two contributions: the first is the proposal of a new model-The associative hierarchical random field (AHRF), and a novel algorithm for its optimization; the second is the application of this model to the problem of semantic segmentation. Most methods for semantic segmentation are formulated as a labeling problem for variables that might correspond to either pixels or segments such as super-pixels. It is well known that the generation of super pixel segmentations is not unique. This has motivated many researchers to use multiple super pixel segmentations for problems such as semantic segmentation or single view reconstruction. These super-pixels have not yet been combined in a principled manner, this is a difficult problem, as they may overlap, or be nested in such a way that the segmentations form a segmentation tree. Our new hierarchical random field model allows information from all of the multiple segmentations to contribute to a global energy. MAP inference in this model can be performed efficiently using powerful graph cut based move making algorithms. Our framework generalizes much of the previous work based on pixels or segments, and the resulting labelings can be viewed both as a detailed segmentation at the pixel level, or at the other extreme, as a segment selector that pieces together a solution like a jigsaw, selecting the best segments from different segmentations as pieces. We evaluate its performance on some of the most challenging data sets for object class segmentation, and show that this ability to perform inference using multiple overlapping segmentations leads to state-of-the-art results.
Collapse
|
19
|
Kafieh R, Rabbani H, Abramoff MD, Sonka M. Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map. Med Image Anal 2013; 17:907-28. [PMID: 23837966 PMCID: PMC3856938 DOI: 10.1016/j.media.2013.05.006] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2012] [Revised: 05/13/2013] [Accepted: 05/17/2013] [Indexed: 01/10/2023]
Abstract
Optical coherence tomography (OCT) is a powerful and noninvasive method for retinal imaging. In this paper, we introduce a fast segmentation method based on a new variant of spectral graph theory named diffusion maps. The research is performed on spectral domain (SD) OCT images depicting macular and optic nerve head appearance. The presented approach does not require edge-based image information in localizing most of boundaries and relies on regional image texture. Consequently, the proposed method demonstrates robustness in situations of low image contrast or poor layer-to-layer image gradients. Diffusion mapping applied to 2D and 3D OCT datasets is composed of two steps, one for partitioning the data into important and less important sections, and another one for localization of internal layers. In the first step, the pixels/voxels are grouped in rectangular/cubic sets to form a graph node. The weights of the graph are calculated based on geometric distances between pixels/voxels and differences of their mean intensity. The first diffusion map clusters the data into three parts, the second of which is the area of interest. The other two sections are eliminated from the remaining calculations. In the second step, the remaining area is subjected to another diffusion map assessment and the internal layers are localized based on their textural similarities. The proposed method was tested on 23 datasets from two patient groups (glaucoma and normals). The mean unsigned border positioning errors (mean ± SD) was 8.52 ± 3.13 and 7.56 ± 2.95 μm for the 2D and 3D methods, respectively.
Collapse
Affiliation(s)
- Raheleh Kafieh
- Department of Physics and Biomedical Engineering, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
| | | | | | | |
Collapse
|
20
|
Le Callet P, Niebur E. Visual Attention and Applications in Multimedia Technologies. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2013; 101:2058-2067. [PMID: 24489403 PMCID: PMC3902206 DOI: 10.1109/jproc.2013.2265801] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Making technological advances in the field of human-machine interactions requires that the capabilities and limitations of the human perceptual system are taken into account. The focus of this report is an important mechanism of perception, visual selective attention, which is becoming more and more important for multimedia applications. We introduce the concept of visual attention and describe its underlying mechanisms. In particular, we introduce the concepts of overt and covert visual attention, and of bottom-up and top-down processing. Challenges related to modeling visual attention and their validation using ad hoc ground truth are also discussed. Examples of the usage of visual attention models in image and video processing are presented. We emphasize multimedia delivery, retargeting and quality assessment of image and video, medical imaging, and the field of stereoscopic 3D images applications.
Collapse
Affiliation(s)
- Patrick Le Callet
- LUNAM Université, Université de Nantes, Institut de Recherche en Communications et Cybernétique de Nantes, Polytech Nantes, UMR CNRS 6597, France
| | - Ernst Niebur
- Solomon Snyder Department of Neuroscience and the Zanvyl Krieger Mind Brain Institute, Johns Hopkins University, Baltimore MD 21218 USA
| |
Collapse
|
21
|
Zhou Q, Zhu J, Liu W. Learning dynamic hybrid Markov random field for image labeling. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2013; 22:2219-2232. [PMID: 23412617 DOI: 10.1109/tip.2013.2246519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Using shape information has gained increasing concerns in the task of image labeling. In this paper, we present a dynamic hybrid Markov random field (DHMRF), which explicitly captures middle-level object shape and low-level visual appearance (e.g., texture and color) for image labeling. Each node in DHMRF is described by either a deformable template or an appearance model as visual prototype. On the other hand, the edges encode two types of intersections: co-occurrence and spatial layered context, with respect to the labels and prototypes of connected nodes. To learn the DHMRF model, an iterative algorithm is designed to automatically select the most informative features and estimate model parameters. The algorithm achieves high computational efficiency since a branch-and-bound schema is introduced to estimate model parameters. Compared with previous methods, which usually employ implicit shape cues, our DHMRF model seamlessly integrates color, texture, and shape cues to inference labeling output, and thus produces more accurate and reliable results. Extensive experiments validate its superiority over other state-of-the-art methods in terms of recognition accuracy and implementation efficiency on: 1) the MSRC 21-class dataset, and 2) the lotus hill institute 15-class dataset.
Collapse
Affiliation(s)
- Quan Zhou
- Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
| | | | | |
Collapse
|
22
|
Corso JJ. Toward parts-based scene understanding with pixel-support parts-sparse pictorial structures. Pattern Recognit Lett 2013. [DOI: 10.1016/j.patrec.2012.04.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
23
|
Context-based global multi-class semantic image segmentation by wireless multimedia sensor networks. Artif Intell Rev 2013. [DOI: 10.1007/s10462-013-9394-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
24
|
|
25
|
Gensure RH, Foran DJ, Lee VM, Gendel VM, Jabbour SK, Carpizo DR, Nosher JL, Yang L. Evaluation of hepatic tumor response to yttrium-90 radioembolization therapy using texture signatures generated from contrast-enhanced CT images. Acad Radiol 2012; 19:1201-7. [PMID: 22841288 DOI: 10.1016/j.acra.2012.04.015] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2012] [Revised: 04/26/2012] [Accepted: 04/26/2012] [Indexed: 10/28/2022]
Abstract
RATIONALE AND OBJECTIVES The aim of this study was to explore the use of texture features generated from liver computed tomographic (CT) datasets as potential image-based indicators of patient response to radioembolization (RE) with yttrium-90 ((90)Y) resin microspheres, an emerging locoregional therapy for advanced-stage liver cancer. MATERIALS AND METHODS Overall posttherapy survival and percent change in serologic tumor marker at 3 months posttherapy represent the primary clinical outcomes in this study. Thirty advanced-stage liver cancer cases (primary and metastatic) treated with RE over a 3-year period were included. Texture signatures for tumor regions, which were delineated to reveal boundaries with normal regions, were computed from pretreatment contrast-enhanced liver CT studies and evaluated for their ability to classify patient serologic response and survival. RESULTS A series of systematic leave-one-out cross-validation studies using soft-margin support vector machine (SVM) classifiers showed hepatic tumor texton and local binary pattern (LBP) signatures both achieve high accuracy (96%) in discriminating subjects in terms of their serologic response. The image-based indicators were also accurate in classifying subjects by survival status (80% and 93% accuracy for texton and LBP signatures, respectively). CONCLUSIONS Hepatic texture signatures generated from tumor regions on pretreatment triphasic CT studies were highly accurate in differentiating among subjects in terms of serologic response and survival. These image-based computational markers show promise as potential predictive tools in candidate evaluation for locoregional therapy such as RE.
Collapse
|
26
|
Parikh D, Zitnick CL, Chen T. Exploring tiny images: the roles of appearance and contextual information for machine and human object recognition. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2012; 34:1978-1991. [PMID: 22201066 DOI: 10.1109/tpami.2011.276] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Typically, object recognition is performed based solely on the appearance of the object. However, relevant information also exists in the scene surrounding the object. In this paper, we explore the roles that appearance and contextual information play in object recognition. Through machine experiments and human studies, we show that the importance of contextual information varies with the quality of the appearance information, such as an image's resolution. Our machine experiments explicitly model context between object categories through the use of relative location and relative scale, in addition to co-occurrence. With the use of our context model, our algorithm achieves state-of-the-art performance on the MSRC and Corel data sets. We perform recognition tests for machines and human subjects on low and high resolution images, which vary significantly in the amount of appearance information present, using just the object appearance information, the combination of appearance and context, as well as just context without object appearance information (blind recognition). We also explore the impact of the different sources of context (co-occurrence, relative-location, and relative-scale). We find that the importance of different types of contextual information varies significantly across data sets such as MSRC and PASCAL.
Collapse
Affiliation(s)
- Devi Parikh
- Toyota Technological Institute in Chicago, 6045 S. Kenwood Ave., Chicago, IL 60637, USA.
| | | | | |
Collapse
|
27
|
Zhang S, Tian Q, Hua G, Huang Q, Gao W. Generating descriptive visual words and visual phrases for large-scale image applications. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2011; 20:2664-2677. [PMID: 21421442 DOI: 10.1109/tip.2011.2128333] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Bag-of-visual Words (BoWs) representation has been applied for various problems in the fields of multimedia and computer vision. The basic idea is to represent images as visual documents composed of repeatable and distinctive visual elements, which are comparable to the text words. Notwithstanding its great success and wide adoption, visual vocabulary created from single-image local descriptors is often shown to be not as effective as desired. In this paper, descriptive visual words (DVWs) and descriptive visual phrases (DVPs) are proposed as the visual correspondences to text words and phrases, where visual phrases refer to the frequently co-occurring visual word pairs. Since images are the carriers of visual objects and scenes, a descriptive visual element set can be composed by the visual words and their combinations which are effective in representing certain visual objects or scenes. Based on this idea, a general framework is proposed for generating DVWs and DVPs for image applications. In a large-scale image database containing 1506 object and scene categories, the visual words and visual word pairs descriptive to certain objects or scenes are identified and collected as the DVWs and DVPs. Experiments show that the DVWs and DVPs are informative and descriptive and, thus, are more comparable with the text words than the classic visual words. We apply the identified DVWs and DVPs in several applications including large-scale near-duplicated image retrieval, image search re-ranking, and object recognition. The combination of DVW and DVP performs better than the state of the art in large-scale near-duplicated image retrieval in terms of accuracy, efficiency and memory consumption. The proposed image search re-ranking algorithm: DWPRank outperforms the state-of-the-art algorithm by 12.4% in mean average precision and about 11 times faster in efficiency.
Collapse
Affiliation(s)
- Shiliang Zhang
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
| | | | | | | | | |
Collapse
|
28
|
Nikolopoulos S, Papadopoulos GT, Kompatsiaris I, Patras I. Evidence-driven image interpretation by combining implicit and explicit knowledge in a Bayesian network. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS. PART B, CYBERNETICS : A PUBLICATION OF THE IEEE SYSTEMS, MAN, AND CYBERNETICS SOCIETY 2011; 41:1366-81. [PMID: 21642042 DOI: 10.1109/tsmcb.2011.2147781] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Computer vision techniques have made considerable progress in recognizing object categories by learning models that normally rely on a set of discriminative features. However, in contrast to human perception that makes extensive use of logic-based rules, these models fail to benefit from knowledge that is explicitly provided. In this paper, we propose a framework that can perform knowledge-assisted analysis of visual content. We use ontologies to model the domain knowledge and a set of conditional probabilities to model the application context. Then, a Bayesian network is used for integrating statistical and explicit knowledge and performing hypothesis testing using evidence-driven probabilistic inference. In addition, we propose the use of a focus-of-attention (FoA) mechanism that is based on the mutual information between concepts. This mechanism selects the most prominent hypotheses to be verified/tested by the BN, hence removing the need to exhaustively test all possible combinations of the hypotheses set. We experimentally evaluate our framework using content from three domains and for the following three tasks: 1) image categorization; 2) localized region labeling; and 3) weak annotation of video shot keyframes. The results obtained demonstrate the improvement in performance compared to a set of baseline concept classifiers that are not aware of any context or domain knowledge. Finally, we also demonstrate the ability of the proposed FoA mechanism to significantly reduce the computational cost of visual inference while obtaining results comparable to the exhaustive case.
Collapse
Affiliation(s)
- Spiros Nikolopoulos
- Centre for Research and Technology Hellas/Informatics and Telematics Institute (CERTH/ITI), Thessaloniki, Greece.
| | | | | | | |
Collapse
|
29
|
Zhang L, Ji Q. Image segmentation with a unified graphical model. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2010; 32:1406-1425. [PMID: 20558874 DOI: 10.1109/tpami.2009.145] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
We propose a unified graphical model that can represent both the causal and noncausal relationships among random variables and apply it to the image segmentation problem. Specifically, we first propose to employ Conditional Random Field (CRF) to model the spatial relationships among image superpixel regions and their measurements. We then introduce a multilayer Bayesian Network (BN) to model the causal dependencies that naturally exist among different image entities, including image regions, edges, and vertices. The CRF model and the BN model are then systematically and seamlessly combined through the theories of Factor Graph to form a unified probabilistic graphical model that captures the complex relationships among different image entities. Using the unified graphical model, image segmentation can be performed through a principled probabilistic inference. Experimental results on the Weizmann horse data set, on the VOC2006 cow data set, and on the MSRC2 multiclass data set demonstrate that our approach achieves favorable results compared to state-of-the-art approaches as well as those that use either the BN model or CRF model alone.
Collapse
Affiliation(s)
- Lei Zhang
- Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
| | | |
Collapse
|
30
|
|
31
|
Liu Q, Xu Q, Zheng VW, Xue H, Cao Z, Yang Q. Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study. BMC Bioinformatics 2010; 11:181. [PMID: 20380733 PMCID: PMC2873531 DOI: 10.1186/1471-2105-11-181] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2009] [Accepted: 04/10/2010] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Gene silencing using exogenous small interfering RNAs (siRNAs) is now a widespread molecular tool for gene functional study and new-drug target identification. The key mechanism in this technique is to design efficient siRNAs that incorporated into the RNA-induced silencing complexes (RISC) to bind and interact with the mRNA targets to repress their translations to proteins. Although considerable progress has been made in the computational analysis of siRNA binding efficacy, few joint analysis of different RNAi experiments conducted under different experimental scenarios has been done in research so far, while the joint analysis is an important issue in cross-platform siRNA efficacy prediction. A collective analysis of RNAi mechanisms for different datasets and experimental conditions can often provide new clues on the design of potent siRNAs. RESULTS An elegant multi-task learning paradigm for cross-platform siRNA efficacy prediction is proposed. Experimental studies were performed on a large dataset of siRNA sequences which encompass several RNAi experiments recently conducted by different research groups. By using our multi-task learning method, the synergy among different experiments is exploited and an efficient multi-task predictor for siRNA efficacy prediction is obtained. The 19 most popular biological features for siRNA according to their jointly importance in multi-task learning were ranked. Furthermore, the hypothesis is validated out that the siRNA binding efficacy on different messenger RNAs(mRNAs) have different conditional distribution, thus the multi-task learning can be conducted by viewing tasks at an "mRNA"-level rather than at the "experiment"-level. Such distribution diversity derived from siRNAs bound to different mRNAs help indicate that the properties of target mRNA have important implications on the siRNA binding efficacy. CONCLUSIONS The knowledge gained from our study provides useful insights on how to analyze various cross-platform RNAi data for uncovering of their complex mechanism.
Collapse
Affiliation(s)
- Qi Liu
- College of Life Science and Biotechnology, Tongji University, China
- Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong
| | - Qian Xu
- Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong
| | - Vincent W Zheng
- Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong
| | - Hong Xue
- Department of Biochemistry, Hong Kong University of Science and Technology, Hong Kong
| | - Zhiwei Cao
- College of Life Science and Biotechnology, Tongji University, China
- Shanghai Center for Bioinformation Technology, China
| | - Qiang Yang
- Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong
| |
Collapse
|
32
|
Quantitative analysis of cryo-EM density map segmentation by watershed and scale-space filtering, and fitting of structures by alignment to regions. J Struct Biol 2010; 170:427-38. [PMID: 20338243 DOI: 10.1016/j.jsb.2010.03.007] [Citation(s) in RCA: 277] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2009] [Revised: 03/14/2010] [Accepted: 03/16/2010] [Indexed: 01/01/2023]
Abstract
Cryo-electron microscopy produces 3D density maps of molecular machines, which consist of various molecular components such as proteins and RNA. Segmentation of individual components in such maps is a challenging task, and is mostly accomplished interactively. We present an approach based on the immersive watershed method and grouping of the resulting regions using progressively smoothed maps. The method requires only three parameters: the segmentation threshold, a smoothing step size, and the number of smoothing steps. We first apply the method to maps generated from molecular structures and use a quantitative metric to measure the segmentation accuracy. The method does not attain perfect accuracy, however it produces single or small groups of regions that roughly match individual proteins or subunits. We also present two methods for fitting of structures into density maps, based on aligning the structures with single regions or small groups of regions. The first method aligns centers and principal axes, whereas the second aligns centers and then rotates the structure to find the best fit. We describe both interactive and automated ways of using these two methods. Finally, we show segmentation and fitting results for several experimentally-obtained density maps.
Collapse
|
33
|
Bub G, Tecza M, Helmes M, Lee P, Kohl P. Temporal pixel multiplexing for simultaneous high-speed, high-resolution imaging. Nat Methods 2010; 7:209-11. [PMID: 20154677 PMCID: PMC2873566 DOI: 10.1038/nmeth.1429] [Citation(s) in RCA: 68] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2009] [Accepted: 01/04/2010] [Indexed: 11/11/2022]
Abstract
We introduce an imaging modality that, by offsetting pixel-exposure times during capture of a single image frame, embeds temporal information in each frame. This allows simultaneous acquisition of full-resolution images at native detector frame rates and high-speed image sequences at reduced resolution, without increasing bandwidth requirements. We demonstrate this method using macroscopic and microscopic examples, including imaging calcium transients in heart cells at 250 Hz using a 10-Hz megapixel camera.
Collapse
Affiliation(s)
- Gil Bub
- Department of Physiology Anatomy and Genetics, Oxford, UK.
| | | | | | | | | |
Collapse
|
34
|
|
35
|
Ladicky L, Russell C, Kohli P, Torr PHS. Graph Cut Based Inference with Co-occurrence Statistics. COMPUTER VISION – ECCV 2010 2010. [DOI: 10.1007/978-3-642-15555-0_18] [Citation(s) in RCA: 105] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
36
|
Yang L, Chen W, Meer P, Salaru G, Goodell LA, Berstis V, Foran DJ. Virtual microscopy and grid-enabled decision support for large-scale analysis of imaged pathology specimens. ACTA ACUST UNITED AC 2009; 13:636-44. [PMID: 19369162 DOI: 10.1109/titb.2009.2020159] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Breast cancer accounts for about 30% of all cancers and 15% of cancer deaths in women. Advances in computer-assisted analysis hold promise for classifying subtypes of disease and improving prognostic accuracy. We introduce a grid-enabled decision support system for performing automatic analysis of imaged breast tissue microarrays. To date, we have processed more than 1,00,000 digitized specimens (1200 x 1200 pixels each) on IBM's World Community Grid (WCG). As a part of the Help Defeat Cancer (HDC) project, we have analyzed that the data returned from WCG along with retrospective patient clinical profiles for a subset of 3744 breast tissue samples, and have reported the results in this paper. Texture-based features were extracted from the digitized specimens, and isometric feature mapping was applied to achieve nonlinear dimension reduction. Iterative prototyping and testing were performed to classify several major subtypes of breast cancer. Overall, the most reliable approach was gentle AdaBoost using an eight-node classification and regression tree as the weak learner. Using the proposed algorithm, a binary classification accuracy of 89% and the multiclass accuracy of 80% were achieved. Throughout the course of the experiments, only 30% of the dataset was used for training.
Collapse
Affiliation(s)
- Lin Yang
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, USA.
| | | | | | | | | | | | | |
Collapse
|
37
|
Pintilie G, Zhang J, Chiu W, Gossard D. Identifying Components in 3D Density Maps of Protein Nanomachines by Multi-scale Segmentation. IEEE/NIH LIFE SCIENCE SYSTEMS AND APPLICATIONS WORKSHOP. IEEE/NIH LIFE SCIENCE SYSTEMS AND APPLICATIONS WORKSHOP 2009; 2009:44-47. [PMID: 20556220 PMCID: PMC2885738 DOI: 10.1109/lissa.2009.4906705] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Segmentation of density maps obtained using cryo-electron microscopy (cryo-EM) is a challenging task, and is typically accomplished by time-intensive interactive methods. The goal of segmentation is to identify the regions inside the density map that correspond to individual components. We present a multi-scale segmentation method for accomplishing this task that requires very little user interaction. The method uses the concept of scale space, which is created by convolution of the input density map with a Gaussian filter. The latter process smoothes the density map. The standard deviation of the Gaussian filter is varied, with smaller values corresponding to finer scales and larger values to coarser scales. Each of the maps at different scales is segmented using the watershed method, which is very efficient, completely automatic, and does not require the specification of seed points. Some detail is lost in the smoothing process. A sharpening process reintroduces detail into the segmentation at the coarsest scale by using the segmentations at the finer scales. We apply the method to simulated density maps, where the exact segmentation (or ground truth) is known, and rigorously evaluate the accuracy of the resulting segmentations.
Collapse
Affiliation(s)
| | - Junjie Zhang
- Structural & Computational, Biology and Molecular, Biophysics, Baylor College of Medicine,
| | - Wah Chiu
- Structural & Computational, Biology and Molecular, Biophysics, Baylor College of Medicine,
| | | |
Collapse
|
38
|
|