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Related Experiment Video

Updated: Feb 19, 2026

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

Using neutrosophic graph cut segmentation algorithm for qualified rendering image selection in thyroid elastography

Yanhui Guo¹, Shuang-Quan Jiang², Baiqing Sun³

¹Department of Computer Science, University of Illinois at Springfield, Springfield, IL USA.

Health Information Science and Systems

|November 8, 2017

Summary

This summary is machine-generated.

This study introduces a new method using neutrosophic graph cuts for selecting high-quality thyroid elastogram images. The algorithm accurately identifies qualified frames, aiding radiologists in thyroid cancer diagnosis.

Keywords:

Elastogram Graph cut Indeterminate filtering Neutrosophic set Thyroid ultrasound

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Area of Science:

Medical Imaging
Computational Imaging
Radiology

Background:

Elastography is crucial for thyroid cancer diagnosis, displaying tissue stress with pseudo-colors.
Variations in elastogram frames hinder manual selection of optimal images by radiologists.
Efficient and accurate selection of qualified elastogram frames is essential for reliable diagnosis.

Purpose of the Study:

To develop an automated method for identifying qualified rendering frames in thyroid elastograms.
To improve the efficiency and accuracy of frame selection for thyroid cancer diagnosis.

Main Methods:

An efficient thyroid ultrasound image segmentation algorithm based on neutrosophic graph cut was employed.
Thyroid ultrasound images were mapped to a neutrosophic set, and an indeterminacy filter reduced image noise.
A graph-based approach with a maximum-flow algorithm segmented the images, followed by anatomic structure identification and color validation.

Main Results:

A dataset of 33 thyroid elastogram cases was used for testing.
The proposed method achieved 100% accuracy in identifying qualified rendering frames.
Manual evaluation by an experienced radiologist confirmed the method's effectiveness.

Conclusions:

The developed neutrosophic graph cut method accurately selects qualified thyroid elastogram frames.
This automated approach assists radiologists in diagnosing thyroid diseases more effectively.
The study highlights the potential of advanced image processing techniques in clinical radiology.