groups and increases slightly; the bus-based scheme is less efficient than the static
grouping-based scheme due to the loss of data transmission, but its advantage lies in its
execution efficiency is not affected by data grouping, and execution efficiency is
maintained with the increase of execution units. In general, the static grouping-based
scheme can achieve good enough results in most cases, but when there are more
execution units and the cost of data transmission is not high, using a bus-based parallelization architecture can achieve better results.
References
1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer
statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers
in 185 countries. CA: Cancer J Clin 68(6):394–424
2. Valente IRS, Cortez PC, Neto EC, Soares JM, de Albuquerque VHC, Tavares JMRS (2016)
Automatic 3D pulmonary nodule detection in CT images: a survey. Comput Methods
Programs Biomed 124:91–107
3. Saxena S, Sharma S, Sharma N (2016) Parallel image processing techniques, benefits and
limitations. Res J Appl Sci Eng Technol 12(2):223–238
4. Ying W, Cunxi C, Tong J, Xinhe X (2009) Segmentation of regions of interest in lung CT
images based on 2-D OTSU optimized by genetic algorithm. In: 2009 Chinese control and
decision conference. IEEE, pp 5185–5189
5. Sasidhar B, Ramesh Babu DR, Bhaskarao N (2013) Automated segmentation of lung regions
and detection of lung cancer in CT scan. Int J Eng Adv Technol 2(4):757–758
6. Dehmeshki J, Amin H, Valdivieso M, Ye X (2008) Segmentation of pulmonary nodules in
thoracic CT scans: a region growing approach. IEEE Trans Med Imaging 27(4):467–480
590
J. Weng et al.
grouping-based scheme due to the loss of data transmission, but its advantage lies in its
execution efficiency is not affected by data grouping, and execution efficiency is
maintained with the increase of execution units. In general, the static grouping-based
scheme can achieve good enough results in most cases, but when there are more
execution units and the cost of data transmission is not high, using a bus-based parallelization architecture can achieve better results.
References
1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer
statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers
in 185 countries. CA: Cancer J Clin 68(6):394–424
2. Valente IRS, Cortez PC, Neto EC, Soares JM, de Albuquerque VHC, Tavares JMRS (2016)
Automatic 3D pulmonary nodule detection in CT images: a survey. Comput Methods
Programs Biomed 124:91–107
3. Saxena S, Sharma S, Sharma N (2016) Parallel image processing techniques, benefits and
limitations. Res J Appl Sci Eng Technol 12(2):223–238
4. Ying W, Cunxi C, Tong J, Xinhe X (2009) Segmentation of regions of interest in lung CT
images based on 2-D OTSU optimized by genetic algorithm. In: 2009 Chinese control and
decision conference. IEEE, pp 5185–5189
5. Sasidhar B, Ramesh Babu DR, Bhaskarao N (2013) Automated segmentation of lung regions
and detection of lung cancer in CT scan. Int J Eng Adv Technol 2(4):757–758
6. Dehmeshki J, Amin H, Valdivieso M, Ye X (2008) Segmentation of pulmonary nodules in
thoracic CT scans: a region growing approach. IEEE Trans Med Imaging 27(4):467–480
590
J. Weng et al.