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Related Experiment Video

Updated: Jul 5, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Learn from orientation prior for radiograph super-resolution: Orientation operator transformer.

Yongsong Huang1, Tomo Miyazaki2, Xiaofeng Liu3

  • 1Department of Communications Engneering, Graduate School of Engineering, Tohoku University, Sendai, 9808579, Japan; Gordon Center for Medical Imaging, Harvard Medical School, Boston, 02114, USA.

Computer Methods and Programs in Biomedicine
|January 18, 2024
PubMed
Summary

A new Orientation Operator Transformer (O²former) enhances radiographic images using an orientation operator and multi-scale feature fusion. This single-image super-resolution model improves diagnostic detail in skeletal muscle disease imaging.

Keywords:
Feature fusionOrientation featureRadiographsSuper-resolution

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Radiology

Background:

  • High-resolution radiographic images are crucial for diagnosing skeletal muscle diseases.
  • Single-image super-resolution (SISR) models show promise for enhancing radiological image quality.
  • Conventional SISR models face challenges with radiographic images due to limited features.

Purpose of the Study:

  • To introduce a novel SISR model, O²former, specifically designed for radiographic images.
  • To address the limitations of conventional SISR models in enhancing low-feature radiographic data.
  • To improve the early diagnosis and treatment of skeletal muscle-related diseases through enhanced image quality.

Main Methods:

  • Incorporation of an orientation operator in the encoder to improve denoising sensitivity and integrate orientation priors.
  • Development of a multi-scale feature fusion strategy to combine features from different receptive fields with directional priors.
  • Proposal of a transformer-based SISR model (O²former) tailored for radiographic image enhancement.

Main Results:

  • The O²former model achieved top-tier performance in objective metrics at a ×4 upsampling factor compared to existing methods.
  • Qualitative analysis revealed enhanced recovery of objective details in the super-resolved radiographic images.
  • The proposed method demonstrates superior capability in reconstructing fine details crucial for medical diagnosis.

Conclusions:

  • The O²former framework offers a novel approach to radiological image super-resolution.
  • The integration of an orientation operator and multi-scale feature fusion significantly boosts reconstruction performance.
  • This advancement holds promise for further progress in the field of radiographic image enhancement.