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4D wavelet noise suppression of MR diffusion tensor data

4D wavelet noise suppression of MR diffusion tensor data

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

作者： Hesamoddin Jahanian Azadeh Yazdan-Shahmorad Hamid Soltanian-Zadeh Department of Radiology Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA Department of Biomedical Engineering University of Michigan Ann Arbor MI USA Controll and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Diffusion tensor imaging (DTI) is known to be promising for providing anatomical information about white-matter fiber bundles that cannot be obtained by other non-invasive in vivo imaging methods. However, its application is limited because of its low signal-to-noise ratio and significant imaging artifacts. To improve the accuracy of tissue structural and architectural characterization with diffusion tensor imaging 4D wavelet denoising technique is used to improve the signal to noise ratio (SNR) of diffusion tensor images. To evaluate the proposed method, a high SNR data set is built by repeating and averaging the data acquisition several times and is compared to the denoised data. Our results revealed that wavelets would effectively reduce the noise in DTI data with less blurring of tissue types, especially in the white matter. It would suggest that by using the 4D wavelet noise suppression, one could decrease the acquisition time and still have an acceptable SNR.

关键词： Tensile stress Diffusion tensor imaging Signal to noise ratio Noise reduction Magnetic noise Anisotropic magnetoresistance Eigenvalues and eigenfunctions In vivo Magnetic resonance imaging Wavelet analysis

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A study on the effect of camera motion on human visual attention

A study on the effect of camera motion on human visual atten...

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IEEE International Conference on image Processing

作者： Golnaz Abdollahian Zygmunt Pizlo Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Department of Psychological Sciences Purdue University West Lafayette IN USA

The aim of this paper is to examine the effect of camera motion in user generated video with respect to human visual attention. Having a more accurate human attention model is particularly useful in applications such as video summarization where the identification of visual importance is crucial to the quality of the results. Most models proposed thus far, have not considered camera motion as an independent factor in identifying visual saliency. In this study eye movement was recorded while subjects watched videos with different types of camera motion. The distribution of the fixation points indicated high correlation between the visual saliency and the type and direction of camera movement. The statistical tests confirmed that the results are statistically significant.

关键词： Cameras Humans Layout Testing Video sequences image processing Laboratories Psychology Information analysis Shape

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Feature fusion as a practical solution toward noncooperative iris recognition

Feature fusion as a practical solution toward noncooperative...

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International Conference on Information Fusion

作者： Nima Tajbakhsh Babak N. Araabi Hamid Soltanianzadeh Control & Intelligent Processing Center of Excellence School of Electrical & Computer Engineering University of Tehran Tehran Iran School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

In noncooperative Iris recognition one should deal with uncontrolled behavior of the subject as well as uncontrolled lighting conditions. That means imperfect focus, contrast, brightness, and orientation among the others. To cope with this situation we propose to take iris images at both near infrared (NIR) and visible light (VL) and use them simultaneously for recognition. In this paper, a novel approach for iris recognition is proposed so that extracted features of NIR and VL images are fused to improve the recognition rate. When the images do not have enough quality due to focus, contrast, etc., effects of feature fusion is more pronounced. This is the situation in UTIRIS database, which is used in our experiments. Experimental results show that the proposed approach, especially in small training samples, leads to a remarkable improvement on recognition rate compared with either NIR or VL recognition.

关键词： Iris Feature extraction Discrete cosine transforms image recognition Databases Training Humans

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Towards an inclusive computational model of visual cortex

Towards an inclusive computational model of visual cortex

引用

IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： M. Gheiratmand H. Soltanian-Zadeh H. Khaloozadeh Electrical Engineering Department K.N. Toosi University of Technology Tehran Iran Radiology Department Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA School of Cognitive Sciences Institute for Studies Theoretical Physics and Mathematics (IPM) Tehran Iran Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran Iran

Understanding the primatespsila visual system has been one of the challenging problems of different groups of scientists for years. Though many studies, from physiology and neuroscience to computer vision, are done on different aspects of visual processing in the cortex, a comprehensive computational model of visual cortex is still missing. We have implemented a computational model of object recognition in ventral visual pathway in our previous work. This hierarchical model covers visual areas V1/V2, V4/PIT, and AIT sending inputs to the Prefrontal Cortex (PFC) for categorization. To extend our model, in this work, we have added a simple model of motion detection in neurons of areas V1 and MT of the dorsal stream to our previous model. This has enabled the model to perform another principal function of the visual cortex, i.e., motion perception.

关键词： Brain modeling Computational modeling Neurons Biological system modeling Object recognition Motion analysis Solid modeling Visual system computer vision Motion detection

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Constrained optimization of nonparametric entropy-based segmentation of brain structures

Constrained optimization of nonparametric entropy-based segm...

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IEEE International Symposium on Biomedical Imaging

作者： Alireza Akhondi Asl Hamid Soltanian-Zadeh School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

We propose a constrained, three-dimensional, nonparametric, entropy-based, coupled, multi-shape approach to segment subcortical brain structures from magnetic resonance images (MRI). The proposed method uses PCA to develop shape models that capture structural variability. It integrates geometrical relationship between different structures into the algorithm by coupling them (limiting their independent deformations). On the other hand, to allow variations among coupled structures, it registers each structure separately when building the shape models. It defines an entropy-based energy function, which is minimized using quasi-Newton algorithm. To this end, probability density functions (pdf) are estimated iteratively using nonparametric Parzen window method. In the optimization algorithm, constraints are used to improve segmentation quality. These constraints are extracted from training data. Sample results are given for the segmentation of caudate, hippocampus, and putamen, illustrating highly superior performance of the proposed method compared to the most similar methods in the literature.

关键词： Constraint optimization Brain Iterative algorithms image segmentation Shape Couplings Magnetic resonance Magnetic resonance imaging Principal component analysis Buildings

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Flagellar swimming for medical micro robots: Theory, experiments and application

Flagellar swimming for medical micro robots: Theory, experim...

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Gabor Kosa Peter Jakab Nobuhiko Hata Ferenc Jolesz Zipi Neubach Moshe Shoham Menashe Zaaroor Gabor Szekely Computer Vision Laboratory Department of Information Technology and Electrical Engineering ETH Zurich Switzerland Harvard Medical School Boston MA USA Division of MRI and Image Guided Therapy Program at Department of Radiology Brigham and Women's Hospital Boston MA USA Faculty of Mechanical Engineering Technion-Israel Institute of Technology Haifa Israel Department of Neurosurgery Rambam Medical Center Haifa Israel

Flagellar swimming is one of the swimming methods used by micro-organisms to advance in aquatic environment. The undulating motion of the flagella was successfully imitated to create propulsion for a medical swimming micro robot. The influence of a head section on the performance of such a robot is shown analytically. Swimming experiments demonstrate forward and backward swimming of a novel magnetically driven swimming tail. We intend to use the flagellar swimming tail in a three tail configuration for neurosurgical intervention in the ventricular system.

关键词： Medical robotics Tail Medical diagnostic imaging Propulsion Magnetosphere Neurosurgery Intestines computer vision Laboratories Information technology

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A Comparative Study of Classification Techniques for Knowledge-Assisted image analysis

A Comparative Study of Classification Techniques for Knowled...

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International Workshop on image analysis for Multimedia Interactive Services, WIAMIS

作者： G. T. Papadopoulos K. Chandramouli V. Mezaris I. Kompatsiaris E. Izquierdo M. G. Strintzis Informatics and Telematics Institute Centre for Research and Technology Hellas Thessaloniki Greece Information Processing Laboratory Electrical & Computer Engineering Department Aristotle University of Thessaloniki Thessaloniki Greece Multimedia and Vision Research Group Queen Mary University of London London UK

In this paper, four individual approaches to region classification for knowledge-assisted semantic image analysis are presented and comparatively evaluated. All of the examined approaches realize knowledge-assisted analysis via implicit knowledge acquisition, i.e. are based on machine learning techniques such as support vector machines (SVMs), self organizing maps (SOMs), genetic algorithm (GA)and particle swarm optimization (PSO). Under all examined approaches, each image is initially segmented and suitable low-level descriptors are extracted for every resulting segment. Then, each of the aforementioned classifiers is applied to associate every region with a predefined high-level semantic concept. An appropriate evaluation framework has been employed for the comparative evaluation of the above algorithms under varying experimental conditions.

关键词： image analysis image segmentation Algorithm design and analysis Knowledge acquisition Machine learning Support vector machines Support vector machine classification Self organizing feature maps Genetic algorithms Particle swarm optimization

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Using a patient image archive to diagnose retinopathy

Using a patient image archive to diagnose retinopathy

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

作者： Kenneth W. Tobin Michael D. Abramoff Edward Chaum Luca Giancardo V. Priya Govindasamy Thomas P. Karnowski Matthew T.S. Tennant Stephen Swainson Image Science and Machine Vision Group Oak Ridge National Laboratory Oak Ridge TN USA Department of Electrical and Computer Engineering University of Iowa Iowa IA USA Department of Ophthalmology and Visual Sciences University of Iowa Hospitals and Clinics Iowa IA USA Tennessee Health Science Center Hamilton Eye Institute Memphis TN USA Oak Ridge National Laboratory Oak Ridge TN USA Department of Ophthalmology University of Alberta Edmonton Canada Faculty of Medicine University of Alberta AB Canada

Diabetes has become an epidemic that is expected to impact 365 Million people worldwide by 2025. Consequently, diabetic retinopathy is the leading cause of blindness in the industrialized world today. If detected early, treatments can preserve vision and significantly reduce debilitating blindness. Through this research we are developing and testing a method for automating the diagnosis of retinopathy in a screening environment using a patient archive and digital fundus imagery. We present an overview of our content-based image retrieval (CBIR) approach and provide performance results for a dataset of 98 images from a study in Canada when compared to an archive of 1,355 patients from a study in the Netherlands. An aggregate performance of 89% correct diagnosis is achieved, demonstrating the potential of automated, web-based diagnosis for a broad range of imagery collected under different conditions and with different cameras.

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Automatic working area classification in peripheral blood smears without cell central zone extraction

Automatic working area classification in peripheral blood sm...

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

作者： W. Xiong J.H. Lim S.H. Ong N.N. Tung J. Liu D. Racoceanu K. Tan A. Chong K. Foong Institute for Infocomm Research Agency for Science Technology and Research Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore IPAL Vietnam National University Hochiminh City Vietnam The French National Research Center (CNRS) and IPAL (Image Perception Access & Language) French-Singaporean Laboratory from Singapore University of Besancon France Department of Microbiology National University of Singapore Singapore Faculty of Dentistry National University of Singapore Singapore

In this paper we study automatic classification of working areas in peripheral blood smears using image analysis and recognition methods. Such automatic classification can provide objective and reproducible quality control for the evaluation of smears and smear maker devices. However, research in this filed has drawn little attention. Existing methods either can not differentiate correctly different cell distributions or rely on the extraction of the central pallor zones in cells for counting, which are not always observable. In contrast, we do not rely on the pallor zone extraction thus on more general basis. We introduce two generic parameters to measure the goodness of working areas, one for the degree of overlap, and the other for the spatial occupancy. We also propose a cascading classification network for the classification of different areas. The effectiveness of our method has been tested on over 150 labeled images acquired from three malaria-infected Giemsa-stained blood smears using an oil immersion 100× objective.

关键词： Blood Area measurement Morphology Oils image color analysis Indexes Training data

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Updated radiometric calibration for the Landsat-5 thematic mapper reflective bands

Updated radiometric calibration for the Landsat-5 thematic m...

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作者： Helder, Dennis L. Markham, Brian L. Thome, Kurtis J. Barsi, Julia A. Chander, Gyanesh Malla, Rimy Department of Electrical Engineering and Computer Science South Dakota State University Brookings SD 57007 United States Landsat Project Science Office NASA Goddard Space Flight Center Greenbelt MD 20771 United States Remote Sensing Group Optical Sciences Center University of Arizona Tucson AZ 85721 United States Science Systems and Applications Inc. NASA Goddard Space Flight Center Greenbelt MD 20771 United States Science Applications International Corporation U.S. Geological Survey Center for Earth Resources Observation and Science Sioux Falls SD 57198 United States Image Processing Laboratory South Dakota State University Brookings SD 57007 United States

The Landsat-5 Thematic Mapper (TM) has been the workhorse of the Landsat system. Launched in 1984, it continues collecting data through the time frame of this paper. Thus, it provides an invaluable link to the past history of the land features of the Earth's surface, and it becomes imperative to provide an accurate radiometric calibration of the reflective bands to the user community. Previous calibration has been based on information obtained from prelaunch, the onboard calibrator, vicarious calibration attempts, and cross-calibration with Landsat-7. Currently, additional data sources are available to improve this calibration. Specifically, improvements in vicarious calibration methods and development of the use of pseudoinvariant sites for trending provide two additional independent calibration sources. The use of these additional estimates has resulted in a consistent calibration approach that ties together all of the available calibration data sources. Results from this analysis indicate a simple exponential, or a constant model may be used for all bands throughout the lifetime of Landsat-5 TM. Where previously time constants for the exponential models were approximately one year, the updated model has significantly longer time constants in bands 1-3. In contrast, bands 4, 5, and 7 are shown to be best modeled by a constant. The models proposed in this paper indicate calibration knowledge of 5% or better early in life, decreasing to nearly 2% later in life. These models have been implemented at the U.S. Geological Survey Earth Resources Observation and Science (EROS) and are the default calibration used for all Landsat TM data now distributed through EROS. © 2008 IEEE.

关键词： Landsat

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