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Updated: May 28, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation.

Shadi Alzubi1, Naveed Islam, Maysam Abbod

  • 1Department of Electronic and Computer Engineering, School of Engineering and Design, Brunel University, West London UB8 3PH, UK.

International Journal of Biomedical Imaging
|October 1, 2011
PubMed
Summary
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Deep learning with multiresolution handcrafted features for brain MRI segmentation.

Artificial intelligence in medicine·2022

This study introduces an automatic medical image segmentation system using curvelet transforms for improved cancer classification. The novel approach enhances abnormal tissue identification and reduces noise in PET, CT, and MRI scans.

Area of Science:

  • Medical Imaging
  • Image Processing
  • Computational Biology

Background:

  • Classifying cancerous tissues in medical scans (PET, CT, MRI) is challenging due to organ shape variations and overlapping gray-level intensities.
  • Existing segmentation methods struggle with the complex nature of soft tissues and anatomical changes across scan slices.
  • Multiresolution analysis (MRA) offers potential for enhanced feature extraction in medical image analysis.

Purpose of the Study:

  • To develop an automatic image segmentation system for classifying regions of interest (ROIs) in medical images.
  • To evaluate the efficacy of curvelet transforms, a novel extension of wavelet and ridgelet transforms, for medical image segmentation.
  • To improve the accuracy of cancer classification and reduce noise in medical scans.

Main Methods:

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Last Updated: May 28, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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Published on: November 30, 2022

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14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

  • The study employed multiresolution analysis (MRA) incorporating wavelet, ridgelet, and curvelet transforms.
  • Curvelet transforms were specifically investigated for their ability to analyze features along curves, addressing limitations of previous methods.
  • The proposed system was tested on medical datasets from PET, CT, and MRI scanners.

Main Results:

  • Curvelet transforms demonstrated a significant improvement in classifying abnormal tissues compared to wavelet and ridgelet transforms.
  • The use of curvelet transforms effectively reduced surrounding noise in the analyzed medical scans.
  • The system showed enhanced capability in segmenting and classifying regions of interest in complex medical images.

Conclusions:

  • Curvelet transform-based segmentation offers a superior method for analyzing medical images, particularly for cancer detection.
  • The developed automatic system provides a promising tool for enhancing diagnostic accuracy in radiology.
  • Further research into curvelet transform applications could advance automated medical image analysis and interpretation.