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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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3D Segmentation Algorithms for Computerized Tomographic Imaging: a Systematic Literature Review.

L E Carvalho1,2, A C Sobieranski3,4, A von Wangenheim5,3

  • 1Graduate Program in Computer Science - Federal University of Santa Catarina, Florianopolis, Brazil. lcarvalho@incod.ufsc.br.

Journal of Digital Imaging
|June 20, 2018
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Summary
This summary is machine-generated.

This review analyzes 3D segmentation algorithms for computerized tomographic imaging, categorizing methods by application and strategy. It provides an overview of techniques and future directions for tomographic image analysis.

Keywords:
3D segmentationComputerized tomographic imagingKitchenham’s systematic reviewSegmentation methods categorization

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Image Processing

Background:

  • Computerized tomographic (CT) imaging generates 3D datasets crucial for medical diagnosis.
  • Accurate 3D segmentation of CT images is essential for quantitative analysis and treatment planning.
  • A systematic review is needed to consolidate current 3D segmentation algorithm knowledge for CT imaging.

Purpose of the Study:

  • To systematically review and categorize 3D segmentation algorithms applied to computerized tomographic imaging.
  • To analyze segmentation methods based on application, algorithmic strategy, validation, and use of prior knowledge.
  • To provide an overview, discussion, and future prospects for these algorithms.

Main Methods:

  • Systematic literature review methodology (Kitchenham).
  • Search conducted across Science Direct, IEEEXplore, ACM, and PubMed databases.
  • Inclusion criteria focused on articles published between 2006 and March 2018.

Main Results:

  • Categorization of 3D segmentation methods by application, algorithmic strategy, validation, and prior knowledge utilization.
  • Detailed conceptual descriptions of analyzed segmentation techniques.
  • Identification of trends and gaps in the field of 3D CT image segmentation.

Conclusions:

  • The review synthesizes the state-of-the-art in 3D segmentation for CT imaging.
  • It highlights diverse algorithmic approaches and their specific applications.
  • Identified challenges and future research avenues for improving 3D segmentation accuracy and efficiency in CT scans.