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

Ming Yan Guo; Hui Jun Cheng; Chun Yuan Lin; Yen Jen Lin; Ann Shyn Chiang

doi:10.1007/978-3-030-30143-9_24

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

Ming Yan Guo, Hui Jun Cheng, Chun Yuan Lin^*, Yen Jen Lin, Ann Shyn Chiang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Pervasive Systems, Algorithms and Networks - 16th International Symposium, I-SPAN 2019, Proceedings
Editors	Christian Esposito, Jiman Hong, Kim-Kwang Raymond Choo
Publisher	Springer
Pages	294-303
Number of pages	10
ISBN (Print)	9783030301422
DOIs	https://doi.org/10.1007/978-3-030-30143-9_24
State	Published - 2019
Event	16th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2019 - Naples, Italy Duration: 16 09 2019 → 20 09 2019

Publication series

Name	Communications in Computer and Information Science
Volume	1080 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	16th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2019
Country/Territory	Italy
City	Naples
Period	16/09/19 → 20/09/19

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Keywords

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

Access to Document

10.1007/978-3-030-30143-9_24

Cite this

Guo, M. Y., Cheng, H. J., Lin, C. Y., Lin, Y. J., & Chiang, A. S. (2019). Computing Image Intersection and Union Regions for Drosophila Neurons Based on Multi-core CPUs. In C. Esposito, J. Hong, & K.-K. R. Choo (Eds.), Pervasive Systems, Algorithms and Networks - 16th International Symposium, I-SPAN 2019, Proceedings (pp. 294-303). (Communications in Computer and Information Science; Vol. 1080 CCIS). Springer. https://doi.org/10.1007/978-3-030-30143-9_24

Guo, Ming Yan ; Cheng, Hui Jun ; Lin, Chun Yuan et al. / Computing Image Intersection and Union Regions for Drosophila Neurons Based on Multi-core CPUs. Pervasive Systems, Algorithms and Networks - 16th International Symposium, I-SPAN 2019, Proceedings. editor / Christian Esposito ; Jiman Hong ; Kim-Kwang Raymond Choo. Springer, 2019. pp. 294-303 (Communications in Computer and Information Science).

@inproceedings{4ce5b0e7f67c48ee9f674142154cc945,

title = "Computing Image Intersection and Union Regions for Drosophila Neurons Based on Multi-core CPUs",

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, OpenMP, Parallel processing",

author = "Guo, {Ming Yan} and Cheng, {Hui Jun} and Lin, {Chun Yuan} and Lin, {Yen Jen} and Chiang, {Ann Shyn}",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 16th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2019 ; Conference date: 16-09-2019 Through 20-09-2019",

year = "2019",

doi = "10.1007/978-3-030-30143-9_24",

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Guo, MY, Cheng, HJ, Lin, CY, Lin, YJ & Chiang, AS 2019, Computing Image Intersection and Union Regions for Drosophila Neurons Based on Multi-core CPUs. in C Esposito, J Hong & K-KR Choo (eds), Pervasive Systems, Algorithms and Networks - 16th International Symposium, I-SPAN 2019, Proceedings. Communications in Computer and Information Science, vol. 1080 CCIS, Springer, pp. 294-303, 16th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2019, Naples, Italy, 16/09/19. https://doi.org/10.1007/978-3-030-30143-9_24

Computing Image Intersection and Union Regions for Drosophila Neurons Based on Multi-core CPUs. / Guo, Ming Yan; Cheng, Hui Jun; Lin, Chun Yuan et al.
Pervasive Systems, Algorithms and Networks - 16th International Symposium, I-SPAN 2019, Proceedings. ed. / Christian Esposito; Jiman Hong; Kim-Kwang Raymond Choo. Springer, 2019. p. 294-303 (Communications in Computer and Information Science; Vol. 1080 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Computing Image Intersection and Union Regions for Drosophila Neurons Based on Multi-core CPUs

AU - Guo, Ming Yan

AU - Cheng, Hui Jun

AU - Lin, Chun Yuan

AU - Lin, Yen Jen

AU - Chiang, Ann Shyn

PY - 2019

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N2 - 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.

AB - 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.

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A2 - Hong, Jiman

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Guo MY, Cheng HJ, Lin CY, Lin YJ, Chiang AS. Computing Image Intersection and Union Regions for Drosophila Neurons Based on Multi-core CPUs. In Esposito C, Hong J, Choo KKR, editors, Pervasive Systems, Algorithms and Networks - 16th International Symposium, I-SPAN 2019, Proceedings. Springer. 2019. p. 294-303. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-30143-9_24

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

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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