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Liver segmentation in MRI: A fully automatic method based on stochastic partitions.

F López-Mir1, V Naranjo1, J Angulo2

  • 1Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.

Computer Methods and Programs in Biomedicine
|February 18, 2014
PubMed
Summary

A novel automated method for liver segmentation in magnetic resonance imaging (MRI) was developed using a stochastic watershed transform. This technique significantly improves accuracy for robust liver segmentation in medical images.

Keywords:
Liver segmentationMagnetic resonance imagingMathematical morphologyStochastic partitionsWatershed transform

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Image Processing

Background:

  • Fully automated liver segmentation in MRI is limited, hindering the use of MRI's advantages over CT.
  • Accurate liver segmentation is crucial for medical diagnosis and treatment planning.

Purpose of the Study:

  • To present a new, fully automated method for liver segmentation in MRI.
  • To enhance segmentation accuracy using a novel stochastic watershed variant.

Main Methods:

  • A marker-controlled watershed transform to reduce oversegmentation.
  • A new variant of stochastic watershed to enhance image gradients.
  • A final classifier to generate the liver mask.

Main Results:

  • The method achieved a high Jaccard coefficient of 0.91 ± 0.02 on 17 datasets.
  • Demonstrated robustness and improved accuracy compared to existing methods.
  • Optimal parameters were tuned on a training dataset.

Conclusions:

  • The proposed stochastic watershed variant is a robust tool for automatic liver segmentation in MRI.
  • This method addresses the need for efficient and accurate automated liver segmentation in medical imaging.