Enhancing semantic segmentation in chest X-ray images through image preprocessing: ps-KDE for pixel-wise substitution by kernel density estimation.

Journal: PloS one
PMID: 38913621

Abstract

BACKGROUND: In medical imaging, the integration of deep-learning-based semantic segmentation algorithms with preprocessing techniques can reduce the need for human annotation and advance disease classification. Among established preprocessing techniques, Contrast Limited Adaptive Histogram Equalization (CLAHE) has demonstrated efficacy in improving segmentation algorithms across various modalities, such as X-rays and CT. However, there remains a demand for improved contrast enhancement methods considering the heterogeneity of datasets and the various contrasts across different anatomic structures.

Authors

  • Yuanchen Wang
    Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America.
  • Yujie Guo
    Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, People's Republic of China.
  • Ziqi Wang
    The Center for Ion Beam Bioengineering & Green Agriculture, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China.
  • Linzi Yu
    Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America.
  • Yujie Yan
    Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States.
  • Zifan Gu
    Quantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas.

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