2017 JRNL PP KUMARADEVAN PUNITHAKUMAR - 1.pdf
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Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
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.