The Experts below are selected from a list of 50508 Experts worldwide ranked by ideXlab platform

Sanjiv Sam Gambhir - One of the best experts on this subject based on the ideXlab platform.

  • amide a free software tool for multimodality Medical Image analysis
    Molecular Imaging, 2003
    Co-Authors: Andreas Markus Loening, Sanjiv Sam Gambhir
    Abstract:

    Amide's a Medical Image Data Examiner (AMIDE) has been developed as a user-friendly, open-source software tool for displaying and analyzing multimodality volumetric Medical Images. Central to the p...

  • AMIDE: A Free Software Tool for Multimodality Medical Image Analysis
    Molecular Imaging, 2003
    Co-Authors: Andreas Markus Loening, Sanjiv Sam Gambhir
    Abstract:

    Amide's a Medical Image Data Examiner (AMIDE) has been developed as a user-friendly, open-source software tool for displaying and analyzing multimodality volumetric Medical Images. Central to the package's abilities to simultaneously display multiple Data sets (e.g., PET, CT, MRI) and regions of interest is the on-demand Data reslicing implemented within the program. Data sets can be freely shifted, rotated, viewed, and analyzed with the program automatically handling interpolation as needed from the original Data. Validation has been performed by comparing the output of AMIDE with that of several existing software packages. AMIDE runs on UNIX, Macintosh OS X, and Microsoft Windows platforms, and it is freely available with source code under the terms of the GNU General Public License.

K Thanushkodi - One of the best experts on this subject based on the ideXlab platform.

  • a secure fast 2d discrete fractional fourier transform based Medical Image compression using spiht algorithm with huffman encoder
    International Review on Computers and Software, 2013
    Co-Authors: Vasanthi P Kumari, K Thanushkodi
    Abstract:

    The arrival of CT and MRI imaging in the last two decades used for analysis of various diseases in Medical field. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) sequences have become essential in healthcare systems and an integral segment of a patient’s Medical record. Such Medical Image Data needed a huge amount of resources for storage and transmission. Compression of Medical Images for efficient use of storage space and transmission bit rate has become a necessity. A quasigroup successfully creates an astronomical number of keys which is used to confuse the hackers in determining the original Data. However, quasi group encryption is not capable in diffusing the information of the plain text.  Hence, in this paper the DICOM Images are encrypted using a chained Hadamard transforms and Number Theoretic Transforms to introduce diffusion along with the quasigroup transformation. A secure Fast Two Dimensional Discrete Fractional Fourier Transform (DFRFT) and a SPIHT Algorithm with Huffman Encoder is used for compress the Image and it provides a secure compression of Medical Images as compared to the other transform. The experimental results evaluate the performance of the proposed encryption approach based on the PSNR and MSE it shows the proposed approach gives better results.

  • a secure fast 2d discrete fractional fourier transform based Medical Image compression using hybrid encoding technique
    International Conference on Current Trends in Engineering and Technology, 2013
    Co-Authors: Vasanthi P Kumari, K Thanushkodi
    Abstract:

    The arrival of CT and MRI imaging in the last two decades used for analysis of various diseases in Medical field. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) sequences have become essential in healthcare systems and an integral segment of a patient's Medical record. Such Medical Image Data needed a huge amount of resources for storage and transmission. Compression of Medical Images for efficient use of storage space and transmission bit rate has become a necessity. A quasigroup successfully creates an astronomical number of keys which is used to confuse the hackers in determining the original Data. However, quasi group encryption is not capable in diffusing the information of the plain text. Hence, in this study the DICOM Images are encrypted using a chained Hadamard transforms and Number Theoretic Transforms to introduce diffusion along with the quasigroup transformation. A secure Fast Two Dimensional Discrete Fractional Fourier Transform (DFRFT) and a SPIHT Algorithm with Huffman Encoder is used for compress the Image and it provides a secure compression of Medical Images as compared to the other transform. The experimental results evaluate the performance of the proposed encryption approach based on the PSNR and MSE it shows the proposed approach gives better results.

Andreas Markus Loening - One of the best experts on this subject based on the ideXlab platform.

  • amide a free software tool for multimodality Medical Image analysis
    Molecular Imaging, 2003
    Co-Authors: Andreas Markus Loening, Sanjiv Sam Gambhir
    Abstract:

    Amide's a Medical Image Data Examiner (AMIDE) has been developed as a user-friendly, open-source software tool for displaying and analyzing multimodality volumetric Medical Images. Central to the p...

  • AMIDE: A Free Software Tool for Multimodality Medical Image Analysis
    Molecular Imaging, 2003
    Co-Authors: Andreas Markus Loening, Sanjiv Sam Gambhir
    Abstract:

    Amide's a Medical Image Data Examiner (AMIDE) has been developed as a user-friendly, open-source software tool for displaying and analyzing multimodality volumetric Medical Images. Central to the package's abilities to simultaneously display multiple Data sets (e.g., PET, CT, MRI) and regions of interest is the on-demand Data reslicing implemented within the program. Data sets can be freely shifted, rotated, viewed, and analyzed with the program automatically handling interpolation as needed from the original Data. Validation has been performed by comparing the output of AMIDE with that of several existing software packages. AMIDE runs on UNIX, Macintosh OS X, and Microsoft Windows platforms, and it is freely available with source code under the terms of the GNU General Public License.

Van M Savage - One of the best experts on this subject based on the ideXlab platform.

  • improving blood vessel tortuosity measurements via highly sampled numerical integration of the frenet serret equations
    IEEE Transactions on Medical Imaging, 2021
    Co-Authors: Alexander B Brummer, David Hunt, Van M Savage
    Abstract:

    Measures of vascular tortuosity—how curved and twisted a vessel is—are associated with a variety of vascular diseases. Consequently, measurements of vessel tortuosity that are accurate and comparable across modality, resolution, and size are greatly needed. Yet in practice, precise and consistent measurements are problematic—mismeasurements, inability to calculate, or contradictory and inconsistent measurements occur within and across studies. Here, we present a new method of measuring vessel tortuosity that ensures improved accuracy. Our method relies on numerical integration of the Frenet-Serret equations. By reconstructing the three-dimensional vessel coordinates from tortuosity measurements, we explain how to identify and use a minimally-sufficient sampling rate based on vessel radius while avoiding errors associated with oversampling and overfitting. Our work identifies a key failing in current practices of filtering asymptotic measurements and highlights inconsistencies and redundancies between existing tortuosity metrics. We demonstrate our method by applying it to manually constructed vessel phantoms with known measures of tortuousity, and 9,000 vessels from Medical Image Data spanning human cerebral, coronary, and pulmonary vascular trees, and the carotid, abdominal, renal, and iliac arteries.

Dimitris N Metaxas - One of the best experts on this subject based on the ideXlab platform.

  • synthetic learning learn from distributed asynchronized discriminator gan without sharing Medical Image Data
    Computer Vision and Pattern Recognition, 2020
    Co-Authors: Qi Chang, Yikai Zhang, Mert R Sabuncu, Chao Chen, Tong Zhang, Dimitris N Metaxas
    Abstract:

    In this paper, we propose a Data privacy-preserving and communication efficient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN). Our proposed framework aims to train a central generator learns from distributed discriminator, and use the generated synthetic Image solely to train the segmentation model. We validate the proposed framework on the application of health entities learning problem which is known to be privacy sensitive. Our experiments show that our approach: 1) could learn the real Image’s distribution from multiple Datasets without sharing the patient’s raw Data. 2) is more efficient and requires lower bandwidth than other distributed deep learning methods. 3) achieves higher performance compared to the model trained by one real Dataset, and almost the same performance compared to the model trained by all real Datasets. 4) has provable guarantees that the generator could learn the distributed distribution in an all important fashion thus is unbiased.We release our AsynDGAN source code at: https://github.com/tommy-qichang/AsynDGAN

  • synthetic learning learn from distributed asynchronized discriminator gan without sharing Medical Image Data
    arXiv: Image and Video Processing, 2020
    Co-Authors: Qi Chang, Yikai Zhang, Mert R Sabuncu, Chao Chen, Tong Zhang, Dimitris N Metaxas
    Abstract:

    In this paper, we propose a Data privacy-preserving and communication efficient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN). Our proposed framework aims to train a central generator learns from distributed discriminator, and use the generated synthetic Image solely to train the segmentation model.We validate the proposed framework on the application of health entities learning problem which is known to be privacy sensitive. Our experiments show that our approach: 1) could learn the real Image's distribution from multiple Datasets without sharing the patient's raw Data. 2) is more efficient and requires lower bandwidth than other distributed deep learning methods. 3) achieves higher performance compared to the model trained by one real Dataset, and almost the same performance compared to the model trained by all real Datasets. 4) has provable guarantees that the generator could learn the distributed distribution in an all important fashion thus is unbiased.

  • engineering and algorithm design for an Image processing api a technical report on itk the insight toolkit
    Medicine Meets Virtual Reality, 2002
    Co-Authors: Michael J Ackerman, Stephen R Aylward, William E Lorensen, William J Schroeder, Vikram Chalana, Dimitris N Metaxas, Ross T Whitaker
    Abstract:

    : We present the detailed planning and execution of the Insight Toolkit (ITK), an application programmers interface (API) for the segmentation and registration of Medical Image Data. This public resource has been developed through the NLM Visible Human Project, and is in beta test as an open-source software offering under cost-free licensing. The toolkit concentrates on 3D Medical Data segmentation and registration algorithms, multimodal and multiresolution capabilities, and portable platform independent support for Windows, Linux/Unix systems. This toolkit was built using current practices in software engineering. Specifically, we embraced the concept of generic programming during the development of these tools, working extensively with C++ templates and the freedom and flexibility they allow. Software development tools for distributed consortium-based code development have been created and are also publicly available. We discuss our assumptions, design decisions, and some lessons learned.