TY - GEN
T1 - GPU-based gray-level co-occurrence matrix for extracting features from magnetic resonance images
AU - Tsai, Hsin Yi
AU - Hanyu, Zhang
AU - Hung, Che Lun
AU - Chen, Hsian Min
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/27
Y1 - 2017/11/27
N2 - With the continuously increasing power of computation, especially in the region of parallel computing, computerbased texture analysis, computer-assisted classification methods, automated pathology detections, etc. are more and more commonly performed on medical images, like X-ray, Magnetic Resonance (MR) images, for clinical or scientific purposes. These procedures almost always include a stage of textural feature extraction, which usually requires an extensive computation. In this paper, we propose a GPGPU (General-purpose computing on graphics processing units)-based parallel method to accelerate the extraction of a set of features based on the Gray-Level Co-Occurrence Matrix (GLCM) which is a second order statistic that characterizes textures. Performance evaluation of the proposed method implemented with CUDA C is carried out on various GPU devices by comparing to its serial counterpart which is implemented in both Matlab and C on a single node. A series of experimental tests focused on Magnetic Resonance (MR) brain images demonstrate that the proposed method is very efficient and superior to the serial counterpart. A speedup of about 30 & 2013; 100 fold is achieved in general.
AB - With the continuously increasing power of computation, especially in the region of parallel computing, computerbased texture analysis, computer-assisted classification methods, automated pathology detections, etc. are more and more commonly performed on medical images, like X-ray, Magnetic Resonance (MR) images, for clinical or scientific purposes. These procedures almost always include a stage of textural feature extraction, which usually requires an extensive computation. In this paper, we propose a GPGPU (General-purpose computing on graphics processing units)-based parallel method to accelerate the extraction of a set of features based on the Gray-Level Co-Occurrence Matrix (GLCM) which is a second order statistic that characterizes textures. Performance evaluation of the proposed method implemented with CUDA C is carried out on various GPU devices by comparing to its serial counterpart which is implemented in both Matlab and C on a single node. A series of experimental tests focused on Magnetic Resonance (MR) brain images demonstrate that the proposed method is very efficient and superior to the serial counterpart. A speedup of about 30 & 2013; 100 fold is achieved in general.
KW - GPGPU
KW - Gray-level co-occurrence matrix
KW - Magnetic resonance imaging (MRI)
KW - Texture features extraction
UR - http://www.scopus.com/inward/record.url?scp=85048276981&partnerID=8YFLogxK
U2 - 10.1109/ISPAN-FCST-ISCC.2017.80
DO - 10.1109/ISPAN-FCST-ISCC.2017.80
M3 - Conference contribution
AN - SCOPUS:85048276981
T3 - Proceedings - 14th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative Computing, ISCC 2017
SP - 391
EP - 396
BT - Proceedings - 14th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative Computing, ISCC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 21 June 2017 through 23 June 2017
ER -