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Renal structural image processing techniques: a systematic review.

Shiva Asadzadeh1, Hamid Tayebi Khosroshahi2, Behzad Abedi3

  • 1a Department of Electrical and Computer Engineering , Tabriz University , Tabriz , Iran.

Renal Failure
|February 13, 2019
PubMed
Summary
This summary is machine-generated.

Image processing of kidney pathology images aids in early diagnosis of renal diseases like nephritis and nephropathy. This review highlights techniques for glomeruli, blood vessels, and tubules, crucial for accurate kidney disorder assessment.

Keywords:
BiopsyGlomeruli segmentationimage analysispathologyrenal

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Area of Science:

  • Medical Imaging
  • Digital Pathology
  • Nephrology

Background:

  • Renal diseases (nephritis, nephropathy) pose significant health risks.
  • Early diagnosis and improved treatment of kidney disorders are critical.
  • Clinical data offers indirect clues; pathological examination provides direct evidence.

Purpose of the Study:

  • To systematically review literature on image processing methods applied to renal pathological microscopic images.
  • To understand the role of advanced image analysis in renal diagnosis and patient care.

Main Methods:

  • Systematic literature review.
  • Analysis of publications utilizing image processing on pathological kidney images.

Main Results:

  • Image analysis techniques focus on: Glomeruli Segmentation and Glomerular Basement Membrane analysis (55%).
  • Other key areas include Blood Vessels and Tubules classification/detection (22%) and Renal Cell Carcinoma grading (22%).

Conclusions:

  • Recommended methods should be auto-adaptive and human-action independent.
  • Clinical validation is essential for accuracy and reliability in medical systems.
  • Future research should explore Moore neighborhood contour tracking and local binary pattern texture analysis.