A review on parallel medical image processing on GPU

Khor, Hui Liang; Liew, Siau-Chuin; Zain, Jasni Mohamad

doi:10.1109/icsecs.2015.7333121

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…The details about the MIMD/SIMD architectural paradigm are also described in [7]. A review based on the utilization of GPU (Graphics Processing Unit) used to speed up medical imaging technique is presented in [8]. The conclusion of the review [8] is that medical image processing methods can be useful by using data parallelism based on GPU processing.…”

Section: Related Workmentioning

confidence: 99%

“…A review based on the utilization of GPU (Graphics Processing Unit) used to speed up medical imaging technique is presented in [8]. The conclusion of the review [8] is that medical image processing methods can be useful by using data parallelism based on GPU processing. A high-performance GPU especially designed for computer games is most beneficial for medical image processing by using a hybrid CPU-GPU combination.…”

Section: Related Workmentioning

confidence: 99%

“…A high-performance GPU especially designed for computer games is most beneficial for medical image processing by using a hybrid CPU-GPU combination. The difference between GPU and CPU and medical image processing based on GPU, described by some researchers are given in [8]. Parallel computing can be completed, either by multi-core computer, distributed computer or workstation computers.…”

Section: Related Workmentioning

confidence: 99%

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A Parallel Processing Technique Based on GMO and BCS for Medical Image Encryption

Singh*,

Singh,

Nath Rai

2020

IJITEE

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Image encryption is a technique that provides security to an image and their data from unauthorized access in which there is the lightweight process (LWP) that can be parallelized resulting in the reduction of computation time. In this paper, parallel lossless image encryption, as well as the decryption technique, is proposed. The method is a parallel implementation of group modulo operation (GMO) based bit circular shift (BCS) of pixel bit-plane values. The backbone of this technique is circular bit rotation based on some modulo group key value. The key value used here is the result of group modulo operation. The binary bit values of pixels of the initial Image are rotated circularly to generate a new binary bit value of pixels encrypted image. The enhancement of this GMO and BCS based encryption are also given here by using the parallel implementation of the algorithm. The given results show the parallel implementation technique has the same level of encryption standard but has a better level of the time standard. As discussed in the result section, this technique can be used for medical image encryption as well as in multimedia applications where the transfer of image data is required over a network.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Parallel Processing Technique Based on GMO and BCS for Medical Image Encryption

Singh*,

Singh,

Nath Rai

2020

IJITEE

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“…, . ) is a binary encoding function which determines the transitions in the local pattern and is given as in (6).…”

Section: Ldp Descriptor and Usage For Image Retrievalmentioning

confidence: 99%

“…GPUs are inexpensive, scalable and have very high computation power compared to single processor CPUs. GPUs have been widely used now for accelerating many efficient CBIR algorithms [4], [5] in the field of image vision, medical imaging [6], remote sensing [7] etc. This paper proposes a parallel implementation of the LDP algorithm on NVIDIA GPUs.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Implementation and Performance Optimization of Local Derivative Pattern Algorithm on GPUs

Chandran

Gangodkar

Mittal

2018

IJECE

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<p><span>Pattern based texture descriptors are widely used in Content Based Image Retrieval (CBIR) for efficient retrieval of matching images. Local Derivative Pattern (LDP), a higher order local pattern operator, originally proposed for face recognition, encodes the distinctive spatial relationships contained in a local region of an image as the feature vector. LDP efficiently extracts finer details and provides efficient retrieval however, it was proposed for images of limited resolution. Over the period of time the development in the digital image sensors had paid way for capturing images at a very high resolution. LDP algorithm though very efficient in content-based image retrieval did not scale well when capturing features from such high-resolution images as it becomes computationally very expensive. This paper proposes how to efficiently extract parallelism from the LDP algorithm and strategies for optimally implementing it by exploiting some inherent General-Purpose Graphics Processing Unit (GPGPU) characteristics. By optimally configuring the GPGPU kernels, image retrieval was performed at a much faster rate. The LDP algorithm was ported on to Compute Unified Device Architecture (CUDA) supported GPGPU and a maximum speed up of around 240x was achieved as compared to its sequential counterpart.</span></p>

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Privacy-Preserving Distributed Similarity Retrieval of Large CT Image Sequence Database in Mobile Telemedicine Networks

Zhuang

Jiang

2022

IEEE Access

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A review on parallel medical image processing on GPU

Cited by 6 publications

References 17 publications

A Parallel Processing Technique Based on GMO and BCS for Medical Image Encryption

A Parallel Processing Technique Based on GMO and BCS for Medical Image Encryption

Real-Time Implementation and Performance Optimization of Local Derivative Pattern Algorithm on GPUs

Privacy-Preserving Distributed Similarity Retrieval of Large CT Image Sequence Database in Mobile Telemedicine Networks

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