Medical image retrieval from distributed environment

Anbarasi, M. S.; Sandhya, Sankaran; Mehata, K. M.; Suganya, V.

doi:10.1109/iama.2009.5228026

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…Due to the limited system scalability, the retrieval performances of the aforementioned single-PC-based CBMIR systems are not satisfactory, especially when dealing with enormous volumes of medical images [17]. Anbarasi et al [18] applied traditional distributed database techniques to a distributed CBMIR system. Due to the powerful parallel processing capability of peer-to-peer (P2P) computation, Charisi et al [19] developed a CBMIR system based on a P2P network.…”

Section: Pr Jmentioning

confidence: 99%

Progressive Distributed and Parallel Similarity Retrieval of Large CT Image Sequences in Mobile Telemedicine Networks

Zhuang

Jiang

2022

Wireless Communications and Mobile Computing

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Computed tomography image (CTI) sequence is essentially a time-series data that typically consists of a large amount of nearby and similar CTIs. Due to the high communication and computational costs, it is difficult to perform a progressive distributed similarity retrieval of the large CTI sequence (CTIS)s, particularly in resource-constraint mobile telemedicine network (MTN)s. In this paper, we present a Dprs method—progressive distributed and parallel similarity retrieval scheme for the CTISs in the MTN. To the best of our knowledge, there is little research on the Dprs processing, especially in the MTN. Four supporting techniques (i.e., (1) PCTI-based similarity measurement, (2) lightweight privacy-preserving strategy, (3) SSL-based data distribution scheme, and (4) the UDI framework) are developed. The experimental evaluation indicates that our proposed Dprs method is more progressive than the state of the art, with a significant reduction in response time.

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Section: Pr Jmentioning

confidence: 99%

Progressive Distributed and Parallel Similarity Retrieval of Large CT Image Sequences in Mobile Telemedicine Networks

Zhuang

Jiang

2022

Wireless Communications and Mobile Computing

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“…In recent years, with the increasing number of medical images in hospital information management systems, different health image-based applications are developed including health information retrieval [5], recommendation and clustering [6]. These applications require a highly efficient access method to support content-based multimedia retrieval at a large scale as one of the most critical media types, medical images and their management, query, and analysis plays a critical role in the modern hospital information management systems [7]. fused PET-CT slice, (D) coronal MR slice, and (E) chest X-ray [8].…”

Section: Introductionmentioning

confidence: 99%

Privacy Preserving Image Encryption Approach for Cloud Storage

Sadik¹

2020

IJATCSE

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Hundreds of thousands of medical images are generated every single year for patients all around the world. The healthcare institutions confront the enormous challene to manage, archive, and retrieve the massive volume of medical images. Cloud is seen as a reliable solution to handle the medical imaging data; however, the need for a medical imaging management system which can preserve privacy and reliable is a must. Therefore, in this paper, a new encryption approach for medical images in cloud storage is proposed to handle privacy preserving image retrieval from the cloud. The proposed encryption approach has utilized the elliptic curve cryptography (ECC) encryption algorithm to encrypt data and store it in the cloud. The proposed approach has compared against other image encryption approaches. Results show that the proposed image encryption approach enhances the speed of the encryption process significantly and manage some powerful security features. The average processing time has decreased from 25 seconds to only 0.1 seconds.

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