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Lung Field Segmentation in Chest X-ray Images Using Superpixel Resizing and Encoder-Decoder Segmentation Networks.

Chien-Cheng Lee1, Edmund Cheung So2, Lamin Saidy1

  • 1Department of Electrical Engineering, Yuan Ze University, Taoyuan 320, Taiwan.

Bioengineering (Basel, Switzerland)
|August 25, 2022
PubMed
Summary

This study introduces a superpixel resizing framework to improve lung segmentation in chest X-rays (CXRs). The method reduces blurring and preserves segmentation quality while speeding up processing on both CPU and GPU.

Keywords:
downsampling interpolationencoder–decoder networklung segmentationsuperpixelsupsampling interpolation

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Area of Science:

  • Medical imaging analysis
  • Computer vision in healthcare

Background:

  • Lung segmentation in chest X-ray (CXR) images is crucial for diagnostics.
  • Current methods often downsample and upsample images, leading to blurred boundaries and reduced segmentation quality.
  • Efficient and accurate lung segmentation is needed for clinical applications.

Purpose of the Study:

  • To develop a novel superpixel resizing framework to enhance lung field segmentation in CXR images.
  • To address the issue of blurred boundaries caused by traditional image resizing techniques.
  • To improve both the speed and quality of high-resolution medical image segmentation.

Main Methods:

  • Incorporation of a superpixel resizing framework with lung field segmentation algorithms.
  • Utilizing superpixel boundary information during the upsampling process to preserve image details.
  • Evaluation on JSRT, LIDC-IDRI, and ANH datasets to validate the proposed method.

Main Results:

  • The superpixel resizing framework significantly outperforms traditional image resizing methods in preserving segmentation quality.
  • The combined approach reduces computation time for high-resolution medical image segmentation.
  • Achieved average processing times of 4.6s on CPU and 0.02s on GPU.

Conclusions:

  • The proposed superpixel resizing framework effectively alleviates blurred boundaries in lung segmentation.
  • This method offers a significant improvement in both speed and quality for CXR analysis.
  • The framework presents a promising advancement for automated diagnostic tools in medical imaging.