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A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems.

ABSTRACT: Image edge detection is a fundamental problem in image processing and computer vision, particularly in the area of feature extraction. However, the time complexity increases squarely with the increase of image resolution in conventional serial computing mode. This results in being unbearably time consuming when dealing with a large amount of image data. In this paper, a novel resolution free parallel implementation algorithm for gradient based edge detection, namely EDENP, is proposed. The key point of our method is the introduction of an enzymatic numerical P system (ENPS) to design the parallel computing algorithm for image processing for the first time. The proposed algorithm is based on a cell-like P system with a nested membrane structure containing four membranes. The start and stop of the system is controlled by the variables in the skin membrane. The calculation of edge detection is performed in the inner three membranes in a parallel way. The performance and efficiency of this algorithm are evaluated on the CUDA platform. The main advantage of EDENP is that the time complexity of O ( 1 ) can be achieved regardless of image resolution theoretically.

SUBMITTER: Yuan J

PROVIDER: S-EPMC6479365 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems.

Yuan Jianying J Guo Dequan D Zhang Gexiang G Paul Prithwineel P Zhu Ming M Yang Qiang Q

Molecules (Basel, Switzerland) 20190329 7

Image edge detection is a fundamental problem in image processing and computer vision, particularly in the area of feature extraction. However, the time complexity increases squarely with the increase of image resolution in conventional serial computing mode. This results in being unbearably time consuming when dealing with a large amount of image data. In this paper, a novel resolution free parallel implementation algorithm for gradient based edge detection, namely EDENP, is proposed. The key p ...[more]

PMID: 30934868

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Project description:BACKGROUND:With the 40th anniversary of the Declaration of Alma-Ata, a global effort is underway to re-focus on strengthening primary health care systems, with emphasis on leveraging community health workers (CHWs) towards the goal of achieving universal health coverage for all. Institutionalizing effective, sustainable community health systems is currently limited by a lack of standard metrics for measuring CHW performance and the systems they work within. Developed through iterative consultations, supported by the Bill & Melinda Gates Foundation and in partnership with USAID and UNICEF, this paper details a framework, list of indicators, and measurement considerations for monitoring CHW performance in low- and middle-income countries. METHODS:A review of peer-reviewed articles, reports, and global data collection tools was conducted to identify key measurement domains in monitoring CHW performance. Three consultations were successively convened with global stakeholders, community health implementers, advocates, measurement experts, and Ministry of Health representatives using a modified Delphi approach to build consensus on priority indicators. During this process, a structured, web-based survey was administered to identify the importance and value of specific measurement domains, sub-domains, and indicators determined through the literature reviews and initial stakeholder consultations. Indicators with more than 75% support from participants were further refined with expert qualitative input. RESULTS:Twenty-one sub-domains for measurement were identified including measurement of incentives for CHWs, supervision and performance appraisal, data use, data reporting, service delivery, quality of services, CHW absenteeism and attrition, community use of services, experience of services, referral/counter-referral, credibility/trust, and programmatic costs. Forty-six indicators were agreed upon to measure the sub-domains. In the absence of complete population enumeration and digitized health information systems, the quality of metrics to monitor CHW programs is limited. CONCLUSIONS:Better data collection approaches at the community level are needed to strengthen management of CHW programs and community health systems. The proposed list of metrics balances exhaustive and pragmatic measurement of CHW performance within primary healthcare systems. Adoption of the proposed framework and associated indicators by CHW program implementors may improve programmatic effectiveness, strengthen their accountability to national community health systems, drive programmatic quality improvement, and plausibly improve the impact of these programs.

Dataset Information

A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems.

Publications

A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems.

Similar Datasets

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