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A Soft-Reference Breast Ultrasound Image Quality Assessment Method That Considers the Local Lesion Area.

Ziwen Wang1,2, Yuxin Song2, Baoliang Zhao2

  • 1School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China.

Bioengineering (Basel, Switzerland)
|August 26, 2023
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Summary

A new global-local image quality assessment (IQA) framework for breast ultrasound images prioritizes lesion region analysis. This method significantly improves diagnostic accuracy compared to traditional global IQA approaches.

Keywords:
breast ultrasoundimage quality assessmentsoft reference

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Diagnostic Accuracy

Background:

  • Breast ultrasound image quality is crucial for accurate disease diagnosis.
  • Current image quality assessment (IQA) methods often overlook the lesion region, focusing on global image features.
  • Clinical evaluation of ultrasound image quality by doctors emphasizes the local lesion area.

Purpose of the Study:

  • To develop a global-local integrated IQA framework for breast ultrasound images that mimics clinical evaluation standards.
  • To improve the accuracy of IQA by incorporating local lesion region assessment.

Main Methods:

  • Collected and annotated 1285 breast ultrasound images by experienced doctors.
  • Classified images into those with and without lesions.
  • Applied soft-reference IQA for images with lesions and bilinear CNN IQA for images without lesions within the integrated framework.

Main Results:

  • The proposed soft-reference IQA for lesion images achieved a PLCC of 0.8418, outperforming existing methods (PLCC 0.6606).
  • The global-local integrated IQA framework demonstrated superior performance, improving the overall PLCC from 0.8306 to 0.8851.
  • The framework accurately captured doctors' clinical evaluation standards for breast ultrasound image quality.

Conclusions:

  • The proposed global-local integrated IQA framework significantly enhances breast ultrasound image quality assessment by focusing on lesion regions.
  • This approach leads to improved diagnostic accuracy and better alignment with clinical expert evaluations.
  • The study highlights the importance of local feature analysis in medical image quality assessment.