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

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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Improving the quality of extracted endmembers

Improving the quality of extracted endmembers

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Workshop on Hyperspectral image and Signal processing: Evolution in remote sensing, WHISPERS

作者： Qian Du Liangpei Zhang Nareenart Raksuntorn Department of Electrical and Computer Engineering Mississippi State University USA State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University of China China

Endmember extraction for spectral mixture analysis is a necessary step when endmember information is unknown. If endmembers are assumed to be pure pixels present in an image scene, endmember extraction is to search the most distinctive pixels. Popular algorithms using the criteria of simplex volume maximization (e.g., N-FINDR) and spectral signature similarity (e.g., Vertex Component Analysis) belong to this type. If pure pixel assumption is not imposed, endmember extraction usually is conducted by searching the signatures that can circumscribe the data cloud with the minimum volume. Both types of algorithms are affected by anomalous pixels since such outliers are very different from other pixels and act as interferers during simplex volume evaluation. In this paper, we propose a new approach that separates the endmember searching in normal and anomalous pixels. Real data experiments show that it can improve the quality of extracted endmembers.

关键词： Data mining Pixel Layout Clouds Laboratories remote sensing Information analysis Spectral analysis Algorithm design and analysis Subspace constraints

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Exploiting statically schedulable regions in dataflow programs

Exploiting statically schedulable regions in dataflow progra...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Ruirui Gu Jorn W. Janneck Mickael Raulet Shuvra S. Bhattacharyya Department of Electrical and Computer Engineering University of Maryland College Park MD USA Xilinx Inc. San Jose CA USA Image and Remote Sensing Group IETR Laboratory UMR CNRS 6164 Rennes France

ISBN: (纸本)9781424423538

Dataflow descriptions have been used in a wide range of Digital Signal processing (DSP) applications, such as multi-media processing, and wireless communications. Among various forms of dataflow modeling, Synchronous Dataflow (SDF) is geared towards static scheduling of computational modules, which improves system performance and predictability. However, many DSP applications do not fully conform to the restrictions of SDF modeling. More general dataflow models, such as CAL, have been developed to describe dynamically-structured DSP applications. Such generalized models can express dynamically changing functionality, but lose the powerful static scheduling capabilities provided by SDF. This paper focuses on detection of SDF-like regions in dynamic dataflow descriptions - in particular, in the generalized specification framework of CAL. This is an important step for applying static scheduling techniques within a dynamic dataflow framework. Our techniques combine the advantages of different dataflow languages and tools, including CAL, DIF and CAL2C. The techniques are demonstrated on the IDCT module of MPEG Reconfigurable Video Coding (RVC).

关键词： Digital signal processing Power system modeling Processor scheduling Dynamic scheduling Aerodynamics Application software Computational modeling Educational institutions Laboratories remote sensing

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Fingerprint Feature Compression Using Statistical Coding Techniques

Fingerprint Feature Compression Using Statistical Coding Tec...

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IEEE India Conference (INDICON)

作者： C. Saravanan Sanjeev S. Malalur Micahel T. Manry Computer Centre National Institute of Technology Durgapur West Bengal India Department of Electrical Engineering Image Processing & Neural Networks Laboratory University of Technology Arlington USA

ISBN: (纸本)9781424448586;9781424448593

We have proposed a new fingerprint feature compression scheme, which extracts the fingerprint feature using pseudo-spectral fusion approach and compresses the fingerprint feature using statistical coding technique. The compression scheme starts with fingerprint image acquisition, enhancement, and normalization. Further, the direction and density of the fingerprint image are extracted. Fusing these two direction and density data results finger print feature data. The fingerprint feature data is applied directly using a statistical coding technique to get compressed data. The Huffman coding and arithmetic coding is used for compression and found arithmetic coding performs better. This proposed compression scheme achieves nearly 85% compression rate, which is higher compared to other present techniques achieved 45% to 63% compression.

关键词： Fingerprint recognition image coding image matching Multiresolution analysis Arithmetic Neural networks Data mining Feature extraction Huffman coding Discrete wavelet transforms

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Gait Recognition Using Wavelet Packet Silhouette Representation and Transductive Support Vector Machines

Gait Recognition Using Wavelet Packet Silhouette Representat...

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Congress on image and Signal processing, CISP

作者： Farzin Dadashi Babak N. Araabi Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Hospital Detroit MI USA

Gait is an idiosyncratic biometric that can be used for human identification at a distance and as a result gained growing interest in intelligent visual surveillance. In this paper, an efficient gait recognition method based on describing subject outer body contour deformations using wavelet packets is proposed. With the use of matching pursuit algorithm, k bases of wavelet packet tree that have maximum similarity to the signal are selected and corresponding coefficients are used as features. Finally, transductive support vector machine (TSVM) classification is utilized on computed eigengait space for semi-supervised identification. The proposed method of selecting features which uses a complete orthogonal or near orthogonal basis from a wavelet packet library of bases and investigating the correlational structure of gait features for each individual using TSVM, result in encouraging identification performance.

关键词： Wavelet packets Support vector machines Matching pursuit algorithms Biometrics Humans Machine intelligence Surveillance Pursuit algorithms Support vector machine classification Libraries

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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

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.

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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A variational framework for 3D colonic polyp visualization in virtual colonoscopy

A variational framework for 3D colonic polyp visualization i...

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IEEE International Conference on image processing

作者： Dongqing Chen Aly A. Farag Robert L. Falk Gerald W. Dryden Computer Vision & Image Processing (CVIP) Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY USA Jewish Hospital Louisville KY USA Division of Gastroenterology/Hepatology University of Louisville Louisville KY USA

Colorectal cancer includes cancer of the colon, rectum, anus and appendix. Since it is largely preventable, it is extremely important to detect and treat the colorectal cancer in the earliest stage. Virtual colonoscopy is an emerging screening technique for colon cancer. One component of virtual colonoscopy, image pre-processing, is important for colonic polyp detection/ diagnosis, feature extraction and classification. This paper introduces a general variational approach based framework for a computer aided diagnosis system for colorectal cancer. It includes techniques for 3D colon segmentation, 3D colon object reconstruction by iso-surface generation, and 3D centerline extraction. The proposed framework has been validated on 22 real CT colonography datasets.

关键词： Colonic polyps Visualization Virtual colonoscopy Cancer Colon Colonography image segmentation Optical computing Computed tomography image reconstruction

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Accurate and fast 3D colon segmentation in CT colonography

Accurate and fast 3D colon segmentation in CT colonography

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IEEE International Symposium on Biomedical Imaging

作者： Dongqing Chen Rachid Fahmi Aly A. Farag Robert L. Falk Gerald W. Dryden Computer Vision & Image Processing (CVIP) Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY USA Department of Medical Imaging Jewish Hospital and Saint Marys Healthcare Louisville KY USA Division of Gastroenterology/Hepatology Department of Medicine University of Louisville Louisville KY USA

ISBN: (纸本)9781424439317

This paper introduces an adaptive level set method for 3D segmentation of colon tissue in CT colonography filled with air and opacified fluid. First, most of the opacified liquid is removed by a threshold value. The closed contours are propagated toward the desired 3D region boundaries through the iterative evolution of the adaptive level sets function. The proposed method has been tested on 22 real CT colonography datasets with various pathologies, and the segmentation accuracy has achieved 98.40%.

关键词： Colonography Colon Virtual colonoscopy Cancer image segmentation Colonic polyps Level set Deformable models Computed tomography Visualization

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Development and Evaluation of Text Localization Techniques Based on Structural Texture Features and Neural Classifiers

Development and Evaluation of Text Localization Techniques B...

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International Conference on Document Analysis and Recognition

作者： Christos Emmanouilidis Costas Batsalas Nikos Papamarkos Computational Systems Unit CETI/R. C. ATHENA Xanthi Greece Image Processing and Multimedia Laboratory Department of Electrical & Computer Engineering Democritus University of Thrace Xanthi Greece Democritus University of Thrace Komotini GR

ISBN: (纸本)9781424445004

This paper presents a text localization approach for binarized printed document images. Emphasis is given to the feature extraction and feature selection stages. In the former, several document structure elements and spatial features, likely to convey useful information, are extracted. In the latter, evolutionary multi-objective feature selection is employed to identify combinations of features with simultaneous good performance in terms of text localization sensitivity and specificity. The selected features are applied to a range of classifiers. Performance results over document image sets from known databases are presented, employing the classifiers with or without feature selection. The results suggest that the hybrid techniques, which utilize the classifiers in combination with the customized pre-processing, feature extraction and feature selection stages, exhibit promising performance on a range of document images.

关键词： Feature extraction Data mining image segmentation Text analysis image analysis image texture analysis Sensitivity and specificity Support vector machines Support vector machine classification image recognition

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Ground truth data collection for unmixing algorithm evaluation

Ground truth data collection for unmixing algorithm evaluati...

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Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral imagery XIV

作者： Rivera-Borrero, Carlos Rosario, Samuel Hunt, Shawn Zayas, Carmen Mundorf, Adrienne Cardona, Suhaily Laboratory for Applied Remote Sensing and Image Processing Electrical and Computer Engineering Department University of Puerto Rico Mayagüez Campus

ISBN: (纸本)9780819471574

This paper presents a ground truth data collection effort along with its use in evaluating unmixing algorithms. Unmixing algorithms are typically evaluated using synthetic data generated by selecting endmember spectrums and adding them in different amounts and with added noise. Going from synthetic to real data poses many problems. One of the greatest is the amount of data to be collected. Also, there will be many unmodeled variations in real data. These include greater variation of the endmembers, additional endmembers that are a very small percentage of the image, and nonlinear effects in the data that are not modeled. The data collation effort produced a high resolution class map along with spectral measurements of 153 different sampling sites to validate the map. The methodology for using this high resolution class map for generating the ground truth data for use in the unmixing algorithms is presented. Specifically, a 1m class map is used to generate the endmember abundances for every pixel in a 30m Hyperion image of the Enrique Reef in Southwest Puerto Rico. The results using two unmixing algorithms, one with a sum to one constraint and the other with a non-negative constraint are presented. The unmixing results for each endmember are presented along with a newly developed unmixing parameter called the Correct Unmixing Index (CUI).

关键词： Data acquisition

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