Dataset Information

A proposed scenario to improve the Ncut algorithm in segmentation.

ABSTRACT: In image segmentation, there are many methods to accomplish the result of segmenting an image into k clusters. However, the number of clusters k is always defined before running the process. It is defined by some observation or knowledge based on the application. In this paper, we propose a new scenario in order to define the value k clusters automatically using histogram information. This scenario is applied to Ncut algorithm and speeds up the running time by using CUDA language to parallel computing in GPU. The Ncut is improved in four steps: determination of number of clusters in segmentation, computing the similarity matrix W, computing the similarity matrix's eigenvalues, and grouping on the Fuzzy C-Means (FCM) clustering algorithm. Some experimental results are shown to prove that our scenario is 20 times faster than the Ncut algorithm while keeping the same accuracy.

SUBMITTER: Tran NY

PROVIDER: S-EPMC10020342 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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A proposed scenario to improve the Ncut algorithm in segmentation.

Tran Nhu Y NY Hieu Huynh Trung HT Bao Pham The PT

Frontiers in big data 20230303

In image segmentation, there are many methods to accomplish the result of segmenting an image into k clusters. However, the number of clusters k is always defined before running the process. It is defined by some observation or knowledge based on the application. In this paper, we propose a new scenario in order to define the value k clusters automatically using histogram information. This scenario is applied to Ncut algorithm and speeds up the running time by using CUDA language to parallel com ...[more]

PMID: 36936997

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Project description:BackgroundCultural competency training has been proposed as a way to improve patient outcomes. There is a need for evidence showing that these interventions reduce health disparities.ObjectiveThe objective was to conduct a systematic review addressing the effects of cultural competency training on patient-centered outcomes; assess quality of studies and strength of effect; and propose a framework for future research.DesignThe authors performed electronic searches in the MEDLINE/PubMed, ERIC, PsycINFO, CINAHL and Web of Science databases for original articles published in English between 1990 and 2010, and a bibliographic hand search. Studies that reported cultural competence educational interventions for health professionals and measured impact on patients and/or health care utilization as primary or secondary outcomes were included.MeasurementsFour authors independently rated studies for quality using validated criteria and assessed the training effect on patient outcomes. Due to study heterogeneity, data were not pooled; instead, qualitative synthesis and analysis were conducted.ResultsSeven studies met inclusion criteria. Three involved physicians, two involved mental health professionals and two involved multiple health professionals and students. Two were quasi-randomized, two were cluster randomized, and three were pre/post field studies. Study quality was low to moderate with none of high quality; most studies did not adequately control for potentially confounding variables. Effect size ranged from no effect to moderately beneficial (unable to assess in two studies). Three studies reported positive (beneficial) effects; none demonstrated a negative (harmful) effect.ConclusionThere is limited research showing a positive relationship between cultural competency training and improved patient outcomes, but there remains a paucity of high quality research. Future work should address challenges limiting quality. We propose an algorithm to guide educators in designing and evaluating curricula, to rigorously demonstrate the impact on patient outcomes and health disparities.

Dataset Information

A proposed scenario to improve the Ncut algorithm in segmentation.

Publications

A proposed scenario to improve the Ncut algorithm in segmentation.

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