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Brain Tumor Segmentation Based on Bendlet Transform and Improved Chan-Vese Model.

Kexin Meng1, Piercarlo Cattani2, Francesco Villecco3

  • 1College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.

Entropy (Basel, Switzerland)
|September 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an automated brain tumor segmentation model using Bendlet transform and an improved Chan-Vese model. The novel approach enhances segmentation accuracy and stability for medical imaging analysis.

Keywords:
Bendlet systemShannon-cosine waveletfeature setimage expressionsegmentation

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Biomedical Engineering

Background:

  • Automated brain tumor segmentation is challenging due to lesion variability and blurred boundaries.
  • Accurate segmentation is crucial for diagnosis, treatment planning, and monitoring of brain tumors.

Purpose of the Study:

  • To develop an automated model for precise brain tumor segmentation.
  • To improve upon existing segmentation methods by incorporating advanced image processing techniques.

Main Methods:

  • Utilized Bendlet transform for feature extraction and multi-scale/directional registration.
  • Employed a Structural Similarity Index Measure (SSIM) based method for preliminary tumor region detection.
  • Applied an improved Chan-Vese (CV) model, solved via a Hermite-Shannon-Cosine wavelet homotopy method, for accurate boundary delineation.

Main Results:

  • The proposed model demonstrated higher segmentation accuracy compared to CV, Ostu, K-FCM, and region growing methods.
  • Experimental results indicated superior stability of the developed algorithm in segmenting brain tumors.
  • The Bendlet transform effectively mapped image features, aiding in distinguishing lesions from normal tissues.

Conclusions:

  • The proposed automated model integrating Bendlet transform and improved CV offers a robust solution for brain tumor segmentation.
  • This method provides more accurate and stable results than traditional techniques, advancing medical image analysis.
  • The study highlights the potential of wavelet-based methods and sparse approximation in medical image segmentation.