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Computing Image Intersection and Union Regions for Drosophila Neurons Based on Multi-core CPUs

  • Ming-Yan Guo
  • Hui-Jun Cheng
  • Chun-Yuan LinEmail author
  • Yen-Jen Lin
  • Ann-Shyn Chiang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1080)

Abstract

With more and more Drosophila Driver and Neuron images, it is an important work to find the similarity relationships among them as the functional inference. There is a general problem that how to find a Drosophila Driver image, which can cover a set of Drosophila Neuron images. In order to solve this problem, the intersection/union region for a set of Drosophila Neuron images should be computed at first, then a comparison work is used to calculate the similarities between the region and Drosophila Driver image(s). In this paper, three encoding schemes, namely Integer, Boolean, Decimal, are proposed to encode each Drosophila Driver and Neuron image as a one-dimensional structure, respectively. Then, the intersection/union region from these images can be computed by using the various operations, such as Boolean operators and lookup-table search method. Finally, the comparison work is done as the union region computation, and the similarity score can be calculated by the definition of Tanimoto coefficient. The above methods for the region computation are also implemented in the multi-core CPUs environment with the OpenMP. From the experimental results, in the encoding phase, the performance by Boolean scheme is the best than that by others; in the region computation phase, the performance by Decimal is the best when the number of images is large. The speedup ratio can achieve 13 based on 16 CPUs.

Keywords

Drosophila Driver image Drosophila Neuron images Intersection/union computation Parallel processing OpenMP 

Notes

Acknowledgement

Part of this work was supported by the Ministry of Science and Technology under the grant MOST 107-2221-E-182-063-MY2 and MOST 107-2218-E-126-001.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ming-Yan Guo
    • 1
  • Hui-Jun Cheng
    • 2
  • Chun-Yuan Lin
    • 1
    • 2
    • 3
    Email author
  • Yen-Jen Lin
    • 4
  • Ann-Shyn Chiang
    • 5
    • 6
    • 7
    • 8
    • 9
  1. 1.Department of Computer Science and Information EngineeringChang Gung UniversityTaoyuanTaiwan
  2. 2.AI Innovation Research CenterChang Gung UniversityTaoyuanTaiwan
  3. 3.Division of Rheumatology, Allergy and ImmunologyChang Gung Memorial HospitalTaoyuanTaiwan
  4. 4.National Center for High-Performance ComputingHsinchuTaiwan
  5. 5.Brain Research CenterNational Tsing Hua UniversityHsinchuTaiwan
  6. 6.Institute of Biotechnology and Department of Life ScienceNational Tsing Hua UniversityHsinchuTaiwan
  7. 7.Kavli Institute for Brain and MindUniversity of California at San DiegoSan DiegoUSA
  8. 8.Department of Biomedical Science and Environmental BiologyKaohsiung Medical UniversityKaohsiungTaiwan
  9. 9.Genomics Research CenterAcademia SinicaNankangTaiwan

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