Open AccessMedical Image Segmentation Using a Combination of Lattice Boltzmann Method and Fuzzy Clustering Based on GPU CUDA Parallel Processing
Author(s) -
Ignasius Boli Suban,
Suyoto Suyoto,
Pranowo Pranowo
Publication year - 2021
Publication title -
international journal of online and biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 8
ISSN - 2626-8493
DOI - 10.3991/ijoe.v17i11.24459
Subject(s) - cuda , computer science , cluster analysis , computational science , lattice boltzmann methods , parallel computing , image segmentation , image processing , segmentation , general purpose computing on graphics processing units , benchmark (surveying) , artificial intelligence , image (mathematics) , graphics , computer graphics (images) , physics , geodesy , quantum mechanics , geography
The rapid development of computer technology has had a significant influence on advances in medical science. This development concerns segmenting medical images that can be used to help doctors diagnose patient diseases. The boundary between objects contained in an image is captured using the level set function. The equation of the level set function is solved numerically by combining the Lattice Boltzmann (LBM) method and fuzzy clustering. Parallel processing using a graphical processing unit (GPU) accelerates the execution of the segmentation process. The results showed that image segmentation with a relatively large size could be done quickly. The use of parallel programming with the GPU can accelerate up to 39.22 times compared to the speed of serial programming with the CPU. In addition, the comparisons with other research and benchmark data show consistent results.