Medical Images Simulation, Storage, and Processing on the European DataGrid Testbed

Montagnat, Johan; Bellet, Fabrice; Benoit-Cattin, Hugues; Breton, Vincent; Brunie, Lionel; Duque, Héctor Mauricio Posada; Legré, Yannick; Magnin, Isabelle E.; Maigne, Lydia; Miguet, Serge; Pierson, Jean‐Marc; Seitz, Lüdwig; Tweed, Tiffany

doi:10.1007/s10723-004-5744-y

Cited by 47 publications

(23 citation statements)

References 27 publications

(27 reference statements)

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“…Then each of the bit planes is shuffled independently using Arnold cat map with different control parameters. The generalized Arnold cat map and its inverse transform are defined in (2), where and are control parameters, , , , are the pixel positions before and after cat mapping, and represents the length or width of a square image, respectively. The control parameters and serve as the secret key of cat map, and the authors use ( , ), (2× , 2× ), .…”

Section: The Medical Image Encryption Scheme Under Studymentioning

confidence: 99%

“…Medical image security is therefore becoming an important issue when digital images and their pertinent patient information are stored and transmitted across public networks [1][2][3][4]. Mandates for ensuring health data security have been issued by the federal government, such as Health Insurance Portability and Accountability Act (HIPAA), enacted by the United states Congress in 1996 [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Cryptanalysis and Improvement of an Efficient and Secure Medical Image Protection Scheme

Zhang

Zhu

Yang

et al. 2015

Mathematical Problems in Engineering

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Nowadays, the increasing demand for telemedicine services has raised interest in the real-time medical image protection literatures. In this paper, we evaluate the security of an efficient and secure medical image protection scheme recently proposed . It is found that this scheme can be successfully broken by launching chosen-plaintext attacks. Improvement is subsequently developed for promoting the security and efficiency performance. Extensive security analyses and experimental results both indicate that the improved scheme can well address the security flaws and advance the speed performance of the original one.

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Section: The Medical Image Encryption Scheme Under Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cryptanalysis and Improvement of an Efficient and Secure Medical Image Protection Scheme

Zhang

Zhu

Yang

et al. 2015

Mathematical Problems in Engineering

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“…This paper also introduces a method called wavelet frames that provide translation of invariant texture analysis. John Montagnat F. Bellet, H. Benoit-Cattin [10] proposed a method that explains Texture-based medical Image Indexing and Retrieval on Grids. C.R.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Model for Content based Image Retrieval

Karthikeyan¹,

Aruna²

2012

IJCA

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In the recent world with the advances in multimedia technologies such as compression, display, and visualization technologies and the increased emphasis on multimedia application, the production of image information has resulted in large volume of images that need to be properly indexed for retrieval in future. Hence, there is a need for Content Based Image retrieval application which makes the retrieval process very efficient. Current systems generally make use of low level features like colour, texture, and shape. In this paper, a novel approach for generalized image retrieval based on semantic contents is presented. A combination of two feature extraction methods namely colour and edge histogram descriptor is proposed. The retrieval efficiency is computed and compared by using four methods such as k-means, colour histogram, edge histogram and sobel method. For colour, the histogram of images is computed and for edge, edge histogram descriptors (EHD) are found. For retrieval of images, a novel idea is developed based on greedy strategy to reduce computational complexity. The proposed system stores the content of database images automatically and query image's content is extracted during runtime and it is used to match against those in database. The result of the query is a set of images that are similar to the query image.

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“…Finally, it connects to the LONI Image Data Archive (IDA), in which the ADNI dataset is stored, among others. Table 2 demonstrate that Grid-based solutions might be an a ppropriate answer to the archiving and c omputational needs of neuroscientists [40][41][42]. Indeed, Grid-based solutions to share data and carry out advanced computational analyses are spreading fast but very few have international capacity.…”

Section: Recent Decentralized Neuroscientific Infrastructuresmentioning

confidence: 99%

Grid Infrastructures for Computational Neuroscience: The neuGRID Example

et al. 2009

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Evolution of brain imaging in neurodegenerative diseasesBrain imaging was regarded as an elective examination in patients with cognitive decline 15 years ago [1]. The practice parameters for diagnosis and evaluation of dementia defined by the American Academy of Neurology regarded computed tomography (CT) and magnetic resonance (MR) as 'optional' assessments [2,3]. Over time, imaging in dementia has moved from a negative, exclusionary role to one that added positive diagnostic and prognostic information. In the late 1990s, the traditional exclusionary approach was abandoned in favor of the inclusive approach [4,5]. Rapid advances in neuroimaging technologies such as PET, single photon emission CT, MR spectroscopy, diffusion tensor imaging and functional MRI have offered new vision into the pathophysiology of Alzheimer's desease (AD) [6] and, consequently, increasingly new powerful data-analysis methods have been developed [7].Since the beginning of the 21st Century, the development of innovative techniques for region-of-interest-based volumetry, automated voxel-based morphometry, cortical thickness measurement, basal forebrain volumetry and multivariate statistics have emerged [7][8][9] and those measurements most feasible and accurate have started to be used in clinical settings. The availability to the neuroimaging community of large prospective image data repositories has led to the development of web-based interfaces to access data and online image analysis tools to assess longitudinal brain changes [10][11][12][13].With the development of novel analysis techniques, the computational complexity of neuroimaging analysis has also increased signifi cantly. Higher spatial resolution images and longer time scans are being acquired so that more voxels will need to be processed for each acquisition. The same applies to the computational resources required by algorithms, since these have become increasingly central processing Neuroscience is increasingly making use of statistical and mathematical tools to extract information from images of biological tissues. Computational neuroimaging tools require substantial computational resources and the increasing availability of large image datasets will further enhance this need. Many efforts have been directed towards creating brain image repositories including the recent US Alzheimer Disease Neuroimaging Initiative. Multisite-distributed computing infrastructures have been launched with the goal of fostering shared resources and facilitating data analysis in the study of neurodegenerative diseases. Currently, some Grid-and non-Grid-based projects are aiming to establish distributed e-infrastructures, interconnecting compatible imaging datasets and to supply neuroscientists with the most advanced information and communication technologies tools to study markers of Alzheimer's and other brain diseases, but they have so far failed to make a difference in the larger neuroscience community. NeuGRID is an Europeon comission-funded effort arising from the needs of the Alzheimer's...

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Medical Images Simulation, Storage, and Processing on the European DataGrid Testbed

Cited by 47 publications

References 27 publications

Cryptanalysis and Improvement of an Efficient and Secure Medical Image Protection Scheme

Cryptanalysis and Improvement of an Efficient and Secure Medical Image Protection Scheme

An Efficient Model for Content based Image Retrieval

Grid Infrastructures for Computational Neuroscience: The neuGRID Example

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