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An interactive medical image segmentation framework using iterative refinement.

Pratik Kalshetti1, Manas Bundele1, Parag Rahangdale1

  • 1Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Jodhpur 342011, Rajasthan, India.

Computers in Biology and Medicine
|February 20, 2017
PubMed
Summary

This study introduces MIST, a two-stage medical image segmentation tool. MIST accurately segments regions of interest in medical images with minimal user interaction.

Keywords:
InteractiveMRIMedical imageMorphologySegmentationX-ray

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Image Processing

Background:

  • Medical image segmentation is crucial for disease identification but challenging due to image irregularities.
  • Conventional methods often require extensive preprocessing and yield unsatisfactory results.
  • Accurate segmentation is vital for effective clinical evaluation and diagnosis.

Purpose of the Study:

  • To propose an automated and efficient medical image segmentation method.
  • To develop a tool that minimizes user interaction while maximizing segmentation accuracy.
  • To address the limitations of conventional segmentation techniques for irregular medical images.

Main Methods:

  • A two-stage algorithm named MIST (Medical Image Segmentation Tool) was developed.
  • Stage one utilizes mathematical morphology to generate a binary marker image.
  • Stage two employs GrabCut with the marker as a mask, refined by a GUI.

Main Results:

  • The MIST algorithm demonstrated accurate and satisfactory segmentation results.
  • The method proved effective on both medical and natural images.
  • Minimal user interaction was required for achieving high-quality segmentation.

Conclusions:

  • MIST offers an efficient and accurate solution for medical image segmentation.
  • The tool effectively handles irregularities common in medical imaging.
  • The proposed method shows potential for clinical applications requiring precise image analysis.