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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Threshold selection using quadtrees.

A Y Wu1, T H Hong, A Rosenfeld

  • 1Computer Vision Laboratory, Computer Science Center, University of Maryland, College Park, MD 20742.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a piecewise constant image approximation method that refines histograms by averaging pixel values within blocks. This technique enhances peak sharpness and aids in easier threshold selection for image segmentation.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Digital Signal Processing

Background:

  • Image analysis often requires simplifying complex image data.
  • Piecewise constant approximations are valuable for reducing image noise and variability.
  • Histogram analysis is crucial for image segmentation and feature extraction.

Purpose of the Study:

  • To develop a method for piecewise constant image approximation.
  • To improve image histograms for easier threshold selection.
  • To explore the utility of block-based averaging for image segmentation.

Main Methods:

  • Recursive image subdivision into quadrants based on gray level standard deviation.
  • Approximation of image blocks with their mean gray level.
  • Analysis of histogram properties before and after approximation.
  • Investigation of small block suppression for histogram valley enhancement.

Main Results:

  • The approximation method yields an image decomposed into blocks of low standard deviation.
  • Approximated images exhibit histograms with sharper peaks due to reduced gray level variability.
  • Suppressing small blocks can deepen histogram valleys, facilitating threshold selection.
  • Using the mean of small block histograms as a threshold is not consistently reliable.

Conclusions:

  • Piecewise constant approximation effectively simplifies images and sharpens histograms.
  • The method offers potential for improved image segmentation through enhanced thresholding.
  • Further research is needed to refine thresholding strategies, particularly for complex images.