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Combinatorial active contour bilateral filter for ultrasound image segmentation.

Anan Nugroho1,2, Risanuri Hidayat1, Hanung A Nugroho1

  • 1Universitas Gadjah Mada, Department of Electrical and Information Engineering, Yogyakarta, Indonesia.

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This study introduces a new computer-aided diagnosis (CAD) framework for segmenting lesions in ultrasound (US) images. The method effectively improves lesion segmentation accuracy for breast and thyroid cancer detection.

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active contourbilateral filtercomputer-aided diagnosisradiologyspeckleultrasound

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Image Segmentation

Background:

  • Computer-aided diagnosis (CAD) in radiological ultrasound (US) imaging is increasingly vital for cancer detection.
  • Accurate segmentation of cancerous lesions in US images is critical for clinical recommendations but challenging due to noise and low contrast.

Purpose of the Study:

  • To develop and evaluate a novel framework for segmenting lesions in breast and thyroid ultrasound images.
  • To enhance the accuracy of lesion segmentation for improved computer-aided diagnosis systems.

Main Methods:

  • A combinatorial framework utilizing a bilateral filter (BF) for image smoothing and edge preservation.
  • A hybrid region-edge-based active contour (AC) model applied globally-to-locally for lesion area capture.
  • Validation on 258 breast and thyroid US images against manual ground truths.

Main Results:

  • The proposed framework achieved high performance in lesion segmentation, quantified by the Dice coefficient.
  • The inclusion of the bilateral filter significantly improved the segmentation framework's performance.
  • Quantitative evaluation demonstrated the effectiveness of the proposed segmentation method.

Conclusions:

  • The developed segmentation framework shows high performance and potential for practical application in CAD radiological US systems.
  • The method offers a robust solution for overcoming segmentation challenges in US imaging.
  • This work contributes to advancing the accuracy and reliability of computer-aided diagnosis in radiology.