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A GPU-ACCELERATED DEFORMABLE IMAGE REGISTRATION ALGORITHM WITH APPLICATIONS TO RIGHT VENTRICULAR SEGMENTATION

Oleh   KUMARADEVAN PUNITHAKUMAR [-]
Kontributor / Dosen Pembimbing : PIERRE BOULANGER; MICHELLE NOGA
Jenis Koleksi : Jurnal elektronik
Penerbit : Lain-lain
Fakultas :
Subjek :
Kata Kunci : GPU computing, cardiac functional analysis, image segmentation, magnetic resonance imaging.
Sumber : https://ieeexplore.ieee.org/Xplore/home.jsp
Staf Input/Edit : Irwan Sofiyan  
File : 1 file
Tanggal Input : 2021-04-05 13:10:38

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2017 JRNL PP KUMARADEVAN PUNITHAKUMAR - 1.pdf ]

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Delineation of the cardiac right ventricle is essential in generating clinical measurements such as ejection fraction and stroke volume. Given manual segmentation on the rst frame, one approach to segment right ventricle from all of the magnetic resonance images is to nd point correspondence between the sequence of images. Finding the point correspondence with non-rigid transformation requires a deformable image registration algorithm, which often involves computationally expensive optimization. The central processing unit (CPU)-based implementation of point correspondence algorithm has been shown to be accurate in delineating organs from a sequence of images in recent studies. The purpose of this study is to develop computationally efcient approaches for deformable image registration. We propose a graphics processing unit (GPU) accelerated approach to improve the efciency. The proposed approach consists of two parallelization components: Parallel compute unied device architecture (CUDA) version of the deformable registration algorithm; and the application of an image concatenation approach to further parallelize the algorithm. Three versions of the algorithm were implemented: 1) CPU; 2) GPU with only intra-image parallelization (sequential image registration); and 3) GPU with inter and intra-image parallelization (concatenated image registration). The proposed methods were evaluated over a data set of 16 subjects. CPU, GPU sequential image, and GPU concatenated image methods took an average of 113.13, 16.50, and 5.96 s to segment a sequence of 20 images, respectively. The proposed parallelization approach offered a computational performance improvement of around 19in comparison to the CPU implementation while retaining the same level of segmentation accuracy. This paper demonstrated that the GPU computing could be utilized for improving the computational performance of a non-rigid image registration algorithm without compromising the accuracy.