Recognition of Parathyroid Nodule by Fusing Prior Knowledge Features in Ultrasound Image

MAO Lin; ZHAO Li-qiang; YU Ming-an; WEI Ying; WANG Ying

doi:10.12263/DZXB.20200271

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Recognition of Parathyroid Nodule by Fusing Prior Knowledge Features in Ultrasound Image

更新时间：2025-12-08

- Recognition of Parathyroid Nodule by Fusing Prior Knowledge Features in Ultrasound Image
- Acta Electronica Sinica Vol. 49, Issue 5, Pages: 944-952(2021)
- 作者机构：
  
  1. 北京化工大学信息科学与技术学院,北京,100029
  2. 中日友好医院介入超声医学科,北京,100029
  3. 中国科学院微电子研究所,北京,100029
  4. 中国科学院大学,北京,100049
  5. 北京化工大学信息科学与技术学院,北京,100029
  6. 中日友好医院介入超声医学科,北京,100029
  7. 中国科学院微电子研究所,北京,100029
  8. 中国科学院大学,北京,100049
- 作者简介：
- 基金信息：
  
  Beijing University of Chemical Technology - China-Japan Friendship Hospital Biomedical Translational Engineering Research Center Joint Fund (No.PYBZ1804)
- DOI：10.12263/DZXB.20200271
  CLC： TP391.4
- Published：2021
- 稿件说明：
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MAO Lin, ZHAO Li-qiang, YU Ming-an, et al. Recognition of Parathyroid Nodule by Fusing Prior Knowledge Features in Ultrasound Image[J]. Acta Electronica Sinica, 2021, 49(5): 944-952.
DOI：

MAO Lin, ZHAO Li-qiang, YU Ming-an, et al. Recognition of Parathyroid Nodule by Fusing Prior Knowledge Features in Ultrasound Image[J]. Acta Electronica Sinica, 2021, 49(5): 944-952. DOI： 10.12263/DZXB.20200271.

摘要

正确识别超声图像中的甲状旁腺结节对甲状旁腺功能亢进的诊断治疗非常重要.由于病人个体的差异性和超声图像的复杂性

采用图像的形态特征和纹理特征识别甲状旁腺结节准确率低.本文提出利用包膜以及结节与甲状腺相对位置的先验知识特征描述方法

并将其与形态、纹理特征融合

采用支持向量数据描述(Support Vector Data Description

SVDD)识别甲状旁腺结节.实验结果表明

先验知识特征可以准确描述甲状旁腺结节的特征

融合先验知识特征比仅利用形态特征和纹理特征具有更高的识别准确率.

Abstract

It is very important to recognize parathyroid nodules correctly in ultrasound images for the treatment of hyperparathyroidism. Due to individual differences of patients and complexity of ultrasound images

parathyroid nodules can’t be recognized accurately by only using morphological features and texture features. In this paper

a prior knowledge feature description method is proposed on account of the characteristic of envelope and the relative location between the nodule and the thyroid. SVDD is applied to recognize parathyroid nodules based on the fusion features of prior knowledge features

morphological features and texture features. The experimental results show that the prior knowledge features can describe the characteristics of parathyroid nodules well

and the accuracy by using the fusion features which combined prior knowledge features is higher than that of only using morphological features and texture features for the recognition of parathyroid nodules.

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