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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Level set method with automatic selective local statistics for brain tumor segmentation in MR images.

Kiran Thapaliya1, Jae-Young Pyun, Chun-Su Park

  • 1Department of Information and Communication Engineering, Chosun University, 375 Seosuk-dong, Dong-gu, Gwangju 501-759, South Korea.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|October 24, 2013
PubMed
Summary

This study introduces an improved level set method for brain tumor segmentation in MRI scans. The new approach efficiently segments tumors by automatically adjusting parameters, enhancing accuracy and robustness.

Keywords:
Active contoursChan–Vese modelGeodesic active contoursImage segmentationLevel set methodMR images

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Area of Science:

  • Medical Imaging
  • Image Segmentation
  • Computational Biology

Background:

  • Image segmentation is crucial for medical diagnosis.
  • Magnetic Resonance Imaging (MRI) is widely used for brain imaging.
  • Accurate segmentation of brain tumors is essential for treatment planning.

Purpose of the Study:

  • To propose an advanced level set method for brain tumor segmentation in MRI.
  • To introduce a novel signed pressure function (SPF) for improved edge detection.
  • To automate parameter calculation for enhanced efficiency and applicability.

Main Methods:

  • Utilized a level set approach for image segmentation.
  • Developed a new signed pressure function (SPF) to handle weak or blurred edges.
  • Incorporated local image statistics for object identification.
  • Implemented automatic adjustment of thresholding values and parameters.

Main Results:

  • The proposed method demonstrated efficient contour stopping at image edges.
  • Tumor objects were accurately identified using local statistics.
  • Automatic parameter calculation improved the algorithm's adaptability to different MR images.
  • Visual and quantitative evaluations confirmed the method's effectiveness.

Conclusions:

  • The developed level set method offers an efficient and robust solution for brain tumor segmentation.
  • Automatic parameter adjustment enhances the method's practical utility.
  • The novel SPF contributes to improved segmentation accuracy, especially with challenging image features.