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Related Experiment Video

Updated: May 23, 2026

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning
08:41

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Published on: July 14, 2020

A novel content-based active contour model for brain tumor segmentation.

Jainy Sachdeva1, Vinod Kumar, Indra Gupta

  • 1Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India. jainysachdeva@gmail.com

Magnetic Resonance Imaging
|March 31, 2012
PubMed
Summary

A new content-based active contour (CBAC) method improves brain tumor segmentation by using both intensity and texture information. This approach overcomes limitations of existing methods, especially for heterogeneous and diffused tumors on MR images.

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Area of Science:

  • Medical imaging analysis
  • Computational anatomy
  • Biomedical engineering

Background:

  • Accurate brain tumor segmentation is vital for treatment planning.
  • Intensity-based active contour models (e.g., GVF, MAC, FVF) struggle with homogeneous tumors against similar backgrounds, partial intensity diversity, false edges, and oversegmentation due to edema.
  • Existing methods fail to segment tumors with weak or diffused edges effectively.

Purpose of the Study:

  • To develop and evaluate a novel content-based active contour (CBAC) method for improved brain tumor segmentation.
  • To address the limitations of intensity-based methods in segmenting challenging brain tumors.
  • To incorporate both intensity and texture information for robust segmentation.

Main Methods:

  • Proposed a content-based active contour (CBAC) model integrating intensity and texture information.

Related Experiment Videos

Last Updated: May 23, 2026

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning
08:41

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Published on: July 14, 2020

  • Utilized the Gray-Level Co-occurrence Matrix (GLCM) to define a texture space for segmentation.
  • Validated the method on real brain MR datasets (55 patients, >600 images) and synthetic images.

    • Main Results:

    • CBAC demonstrated superior performance in segmenting homogeneous, heterogeneous, and diffused brain tumors on various MR sequences (T1, postcontrast T1, T2).
    • The method successfully segmented tumors with uniform intensity, complex content, and surrounding edema.
    • Effective tumor volume extraction was achieved, termed 2.5-dimensional segmentation.

    Conclusions:

    • The proposed CBAC method significantly enhances brain tumor segmentation accuracy compared to traditional intensity-based techniques.
    • Integrating texture information via GLCM provides robustness against image variations and tumor complexities.
    • CBAC offers a promising solution for precise tumor volume assessment in clinical practice.