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Learning Representative Examples for Data Annotation

International Journal of Computer processing of Languages 2009年第2N03期22卷 155-169页

作者： JINGBO ZHU HUIZHEN WANG BENJAMIN K. TSOU Natural Language Processing Laboratory Northeastern University Shenyang Liaoning 110004 P. R. China Key Laboratory of Medical Image Computing (Northeastern University) Ministry of Education Shenyang Liaoning 110004 P. R. China Language Information Sciences Research Centre City University of Hong Kong Hong Kong

Among the techniques to solve the knowledge bottleneck problem of supervised learning models, active learning is a promising method. One of the popular techniques of active learning is uncertainty sampling which, however, often presents problems when outliers are selected. To solve this problem, this paper presents a density-based re-ranking technique, in which a density measure is adopted to determine whether an unlabeled example is an outlier. The motivation of this method is to use not only the most informative example in terms of uncertainty measure, but also the most representative example in terms of density measure. The second effort we made is that a technique of sampling by clustering (SBC) is presented to build a representative initial training data set for active learning. Experimental results of active learning for word sense disambiguation and text classification tasks show that the proposed techniques can improve active learning with uncertainty sampling.

关键词： Active learning uncertainty sampling density-based re-ranking sampling by clustering text classification word sense disambiguation

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Landmark Extraction from Echocardiography Sequence Based on Corner Detection Algorithms Using Product Moment Coefficient Matcher

Landmark Extraction from Echocardiography Sequence Based on ...

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International Conference on signal processing Systems, ICSPS

作者： Meysam Siyah Mansoory Hamid Behnam Emad Fatemi Zadeh Bio Medical Engineering Department Science and Research Branch Islamic Azad University슠 Tehran Iran Bio Medical Engineering Group Electrical Engineering Department Iran University of Science and Technology Tehran Iran Bio Medical Signal and Image Processing Laboratory School of Electrical Engineering Sharif University of Technology Tehran Iran

ISBN: (纸本)9781424442133

Landmark extraction is used as the first step of many vision tasks such as tracking, image registration, localization, image matching and recognition. Furthermore, landmarks are used to reduce the data flow and consequently the computational costs. In this paper we extracted landmarks from echocardiography sequence, our algorithm is based on corner extraction, and then we evaluate our algorithm with applying some test. For this purpose, we evaluated the detectors according to their repeatability, stability and landmark localization under changes in noise.

关键词： Echocardiography Detection algorithms Data mining image registration image matching image recognition Computational efficiency Testing Detectors Stability

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Brain tissue classification using independent vector analysis (IVA) for magnetic resonance image

Brain tissue classification using independent vector analysi...

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2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009

作者： Chiou, Yaw-Jiunn Chen, Hsiang-Min Chai, Jyh Wen Chen, Clayton Chi-Chang Ouyang, Yen-Chieh Su, Wu-Chung Yang, Ching-Wen Lee, San-Kan Chang, Chein-I. Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology College of Medicine China Medical University Taichung Taiwan School of Medicine National Yang-Ming University Taipei Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung 406 Taiwan Computer Center Taichung Veterans General Hospital Taichung 407 Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780769536569

The purpose of this study is to present a new method, independent vector analysis (IVA), by extending independent component analysis (ICA) of univariate source signals to multivariate source signals on Magnetic Resonance Imaging (MRI). IVA is utilized to relief the limitation of the conventional ICA approach. The proposed method can resolve the permutation problem during individual ICA runs for group brain MR images. The proposed IVA method in conjunction with support vector machine (SVM), we can effectively separate the different part of gray, white matter and cerebrospinal fluid (CSF) from brain soft tissues. In order to demonstrate the proposed IVA-SVM method, experiments are conducted for performance analysis and evaluation. Simulation results show that using IVA can greatly release from the problem cause from traditional ICA to the situation of analyzing inconsistent results of MR image. © 2009 IEEE.

关键词： Magnetic resonance imaging

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An automatic computer-aided detection system for meniscal tears on magnetic resonance images

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IEEE Transactions on medical Imaging 2009年第8期28卷 1308-1316页

作者： Ramakrishna, Bharath Liu, Weimin Saiprasad, Ganesh Safdar, Nabile Chang, Chein-I Siddiqui, Khan Kim, W. Siegel, Eliot Chai, Jyh-Wen Chen, Clayton Chi-Chang Lee, San-Kan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD 21250 United States University of Maryland School of Medicine Baltimore MD 21201 United States Department of Electrical Engineering and Environmental Restoration Disaster Reduction Research Center National Chung Hsing University Taichung 402 Taiwan VA Maryland Health Care System Baltimore MD 21201 United States Hospital of the University of Pennsylvania Philadelphia PA 19104 United States Department of Radiology College of Medicine China Medical University Taichung 404 Taiwan Department of Radiology Taichung Veterans General Hospital Taichung 40705 Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung 406 Taiwan Department of Radiology Taichung Veterans General Hospital Taichung 407 Taiwan

Knee-related injuries including meniscal tears are common in both young athletes and the aging population, and require accurate diagnosis and surgical intervention when appropriate. With proper techniques and radiologists' experienced skills, confidence in detection of meniscal tears can be quite high. This paper develops a novel computer-aided detection (CAD) diagnostic system for automatic detection of meniscal tears in the knee. Evaluation of this CAD system using an archived database of images from 40 individuals with suspected knee injuries indicates that the sensitivity and specificity of the proposed CAD system are 83.87% and 75.19%, respectively, compared to the mean sensitivity and specificity of 77.41% and 81.39%, respectively, obtained by experienced radiologists in routine diagnosis without using the CAD. The experimental results suggest that the developed CAD system has great potential and promise in automatic detection of both simple and complex meniscal tears of the knee. © 2009 IEEE.

关键词： Magnetic resonance imaging

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Brain Tissue Classification Using Independent Vector Analysis (IVA) for Magnetic Resonance image

Brain Tissue Classification Using Independent Vector Analysi...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yaw-Jiunn Chiou Hsiang-Min Chen Jyh Wen Chai Clayton Chi-Chang Chen Yen-Chieh Ouyang Wu-Chung Su Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology College of Medicine China Medical University and Hospital Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and technology Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

关键词： Brain image analysis Magnetic analysis Magnetic resonance Independent component analysis Magnetic resonance imaging Support vector machines signal analysis image resolution signal resolution

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processing framework and the fast volume rendering algorithms for out-of-core medical data

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Ruan Jian Xue Bao/Journal of Software 2008年第12期19卷 3237-3248页

作者： Xue, Jian Tian, Jie Dai, Ya-Kang Chen, Jian College of Computing and Communication Engineering Graduate University The Chinese Academy of Sciences Beijing 100049 China Medical Image Processing Group Institute of Automation The Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Complex Systems and Intelligence Science Beijing 100190 China

This paper designs and implements an algorithm framework for the out-of-core medical data processing and analyzing and integrates it into MITK (medical imaging toolkit), an algorithm toolkit for medical image processing and analyzing accomplished by the group. With the help of this, a processing platform for the out-of-core medical data is set up and fast out-of-core volume rendering algorithms based on volume ray casting and 3D texture are studied in this paper. A semi-adaptive partitioning method is proposed to divide original data sets into sub-blocks and get a better partitioning result without influencing the partitioning speed. Furthermore, the graphics hardware is also used to accelerate the rendering process. The experimental results indicate that the new framework and algorithms are effective and efficient for the processing and visualization of the out-of-core medical data sets. © 2008 by Journal of Software.

关键词： Volume rendering

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Design and characterization of the I-ImaS multi-element X-ray detector system

Design and characterization of the I-ImaS multi-element X-ra...

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作者： Griffith, Jennifer A. Metaxas, Marinos G. Royle, Gary J. Venanzi, Cristian Esbrand, Colin Cavouras, Dionisis Fant, Andrea Gasiorek, Przemyslaw Georgiou, Harris Hall, Geoff Jones, John Leaver, James D. G. Longo, Renata Manthos, Nicos Noy, Matthew Østby, Joar Martin Rokvic, Tatjana Schulerud, Helene Theodoridis, Sergios Triantis, Frixos A. Turchetta, Renato Speller, Robert D. IEEE Department of Medical Physics and Bioengineering University College London London WC1E 6BT United Kingdom Department of Physics University of Trieste 34127 Trieste Italy Department of Medical Physics and Bioengineering University College London WClE 6BT London United Kingdom Medical Image and Signal Processing Laboratory Department of Medical Instrument Technology Technological Education Institution of Athens 12210 Athens Greece Rutherford Appleton Laboratory CCLRC OX11 OQX Oxfordshire United Kingdom Department of Informatics and Telecommunications University of Athens 15784 Athens Greece High Energy Physics Group Department of Physics Imperial College SW7 2AZ London United Kingdom Department of Physics University of Ioannina 45110 Ioannina Greece Division of Electronics and Cybernetics SINTEF NO-0314 Oslo Norway

I-ImaS (Intelligent Imaging Sensors) is a European project aiming to produce new, intelligent x-ray imaging systems using novel APS sensors to create optimal diagnostic images. Initial systems have been constructed for medical imaging;specifically mammography and dental encephalography. However, the I-ImaS system concept could be applied to all areas of x-ray imaging, including homeland security and industrial QA. The I-ImaS system intelligence is implemented by the use of APS technology and FPGAs, allowing real-time analysis of data during image acquisition. This gives the system the capability to perform as an on-the-fly adaptive imaging system, with the potential to create images with maximum diagnostic information within given dose constraints. The I-ImaS system uses a scanning linear array of scintillatorcoupled 1.5-D CMOS Active Pixel Sensors to create a full 2-D x-ray image of an object. This paper describes the parameters considered when choosing the scintillator elements of the detectors. A study of the positioning of the sensors to form a linear detector is also considered, along with a discussion of the potential losses in image quality associated with creating a linear sensor by tiling many smaller sensors. Preliminary results show that the detectors have sufficient performance to be used successfully in the initial mammographie and encephalographic I-ImaS systems that are currently under construction. © 2008 IEEE.

关键词： X ray detectors

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Spatial distortion correction and crystal identification for position-sensitive avalanche photodiode-based PET scanners

Spatial distortion correction and crystal identification for...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Abhijit J. Chaudhari Anand A. Joshi Yibao Wu Richard M. Leahy Simon R. Cherry Ramsey D. Badawi Department of Biomedical Engineering University of California슠Davis Davis CA USA Signal and Image Processing Institute University of Southern California Los Angeles CA USA Laboratory of Neuro Imaging University of California Los Angeles Los Angeles CA USA Department of Radiology Davis Medical Center University of California Sacramento CA USA

Position-sensitive avalanche photodiodes (PSAPDs) are gaining widespread acceptance in modern PET scanner designs, and owing to their relative insensitivity to magnetic fields, especially in those that are MRI-compatible. Flood histograms in PET scanners are used to determine the crystal of annihilation photon interaction and hence, for detector characterization and routine quality control. For PET detectors that use PSAPDs, flood histograms show a characteristic pincushion distortion when Anger logic is used for event positioning. A small rotation in the flood histogram is also observed when the detectors are placed in a magnetic field. We first present a general purpose automatic method for spatial distortion correction for flood histograms of PSAPD-based PET detectors when placed both inside and outside a MRI scanner. Analytical formulae derived for this scheme are based on a hybrid approach that combines desirable properties from two existing event positioning schemes. The rotation of the flood histogram due to the magnetic field is determined iteratively and is accounted for in the scheme. We then provide implementation details of a method for crystal identification we have previously proposed and evaluate it for cases when the PET scanner is both outside and in a magnetic field. In this scheme, Fourier analysis is used to generate a lower-order spatial approximation of the distortion-corrected PSAPD flood histogram, which we call the ‘template‘. The template is then registered to the flood histogram using a diffeomorphic iterative intensity-based warping scheme. The calculated deformation field is then applied to the segmentation of the template to obtain a segmentation of the flood histogram. A manual correction tool is also developed for exceptional cases. We present a quantitative assessment of the proposed distortion correction scheme and crystal identification method against conventional methods. Our results indicate that our proposed methods lead to a la

关键词： Positron emission tomography Floods Histograms Magnetic fields Detectors Photonic crystals Magnetic analysis Avalanche photodiodes Quality control Event detection

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Performance of a self-paced brain computer interface on data contaminated with eye-movement artifacts and on data recorded in a subsequent session

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Computational Intelligence and Neuroscience 2008年第unknown期2008卷 1-13页

作者： Fatourechi, Mehrdad Ward, Rabab K. Birch, Gary E. Image and Signal Processing Laboratory Department of Electrical and Computer Engineering University of British Columbia Vancouver BC V6T 1Z4 Canada Institute for Computing Information and Cognitive Systems University of British Columbia Vancouver BC V6T 1Z4 Canada Neil Squire Society Burnaby BC V5M 3Z3 Canada

The performance of a specific self-paced BCI (SBCI) is investigated using two different datasets to determine its suitability for using online: (1) data contaminated with large-amplitude eye movements, and (2) data recorded in a session subsequent to the original sessions used to design the system. No part of the data was rejected in the subsequent session. Therefore, this dataset can be regarded as a pseudo-online test set. The SBCI under investigation uses features extracted from three specific neurological phenomena. Each of these neurological phenomena belongs to a different frequency band. Since many prominent artifacts are either of mostly low-frequency (e.g., eye movements) or mostly high-frequency nature (e.g., muscle movements), it is expected that the system shows a fairly robust performance over artifact-contaminated data. Analysis of the data of four participants using epochs contaminated with large-amplitude eye-movement artifacts shows that the system's performance deteriorates only slightly. Furthermore, the system's performance during the session subsequent to the original sessions remained largely the same as in the original sessions for three out of the four participants. This moderate drop in performance can be considered tolerable, since allowing artifact-contaminated data to be used as inputs makes the system available for users at ALL times.

关键词： .Brain Computer Data Contaminated Eye-Movement Artifacts Subsequent Session Recorded in Subsequent SBCI contaminated with large-amplitude neurological phenomena

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Colour-Texture based image analysis method for assessing the Hormone Receptors status in Breast tissue sections

Colour-Texture based image analysis method for assessing the...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Spiros Kostopoulos Dionisis Cavouras Antonis Daskalakis Panagiotis Bougioukos Pantelis Georgiadis George C. Kagadis Ioannis Kalatzis Panagiota Ravazoula George Nikiforidis Medical Image Processing and Analysis Group (MIPA) Laboratory of Medical Physics School of Medicine University of Patras Rio-Patras Greece Medical Image and Signal Processing Laboratory Department of Medical Instrumentation Technology Technological Education Institute of Athens Athens Greece Department of Pathology University Hospital of Patras Rio-Patras Greece

Hormone receptors have been used in prognosis of breast carcinomas and their positive status is of clinical value in hormonal therapy. Determination of this status is based on the subjective visual inspection of the stained nuclei in the specimens. The aim of this study was the assessment of the estrogen receptor's (ER) positive status of breast carcinomas, by means of colour-texture based image analysis methodology. Twenty two cases of immunohistochemically (IHC) stained breast biopsies were initially assessed by a histopathologist for ER positive status, following a clinical scoring protocol. Custom-designed image analysis software was developed for automatically assessing the ER positive status, employing colour textural features and the k-Nearest Neighbor weighted votes classification algorithm. Computer-based image analysis system resulted in 86.4% overall accuracy and in 0.875 Kendall's coefficient of concordance (p<0.001), ranking correctly 19/22 cases. Colour-texture analysis of IHC stained specimens might have an impact in the quantitative assessment of ER status.

关键词： image color analysis Biochemistry Breast tissue Erbium medical treatment Inspection Immune system Breast biopsy Protocols Voting

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