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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Survey: interpolation methods for whole slide image processing.

L Roszkowiak1, A Korzynska1, J Zak1

  • 1Laboratory of Processing Systems of Microscopic Image Information, Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland.

Journal of Microscopy
|September 30, 2016
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Summary
This summary is machine-generated.

Choosing the right image resizing method is crucial for pathology diagnostics. This study evaluates nine interpolation techniques for whole slide images, finding the best choice depends on the specific task.

Keywords:
Biomedical engineeringimage interpolationimage processingmicroscopic imageswhole slide images

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Area of Science:

  • Digital Pathology
  • Image Analysis
  • Computational Biology

Background:

  • Whole slide images (WSIs) are essential in pathology for diagnosis, grading, and prognosis.
  • Resizing WSIs is a common preprocessing step, often utilizing interpolation methods.
  • Selecting an appropriate interpolation method impacts downstream image quantification.

Purpose of the Study:

  • To evaluate and compare the performance of nine different interpolation methods for resizing WSIs.
  • To determine the optimal interpolation method for preserving image quality and quantification accuracy in digital pathology.
  • To guide the selection of interpolation techniques based on specific WSI processing tasks.

Main Methods:

  • Nine interpolation methods were assessed for their suitability in resizing WSIs.
  • Test images, including general and specialized biological samples, were scaled and rescaled.
  • Performance was evaluated based on image fidelity, quantification accuracy, and computational time.

Main Results:

  • Different interpolation methods exhibit varying degrees of performance in terms of image quality and quantification.
  • The time required for computation differs significantly among the evaluated interpolation techniques.
  • No single interpolation method is universally superior; selection is task-dependent.

Conclusions:

  • The choice of interpolation method for WSI resizing should be tailored to the specific application and quantification goals.
  • Further research may focus on developing adaptive interpolation strategies for digital pathology.
  • Optimizing WSI resizing is critical for reliable automated analysis in pathology.