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Segmentation and Diagnosis of Papillary Thyroid Carcinomas Based on Generalized Clustering Algorithm in Ultrasound Elastography

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Papillary thyroid carcinomas (PTC) are the most common type of thyroid malignant tumors. Existing methods for clustering high-noise ultrasound images tend to degrade the clustering performance. In order to realize accurate segmentation of thyroid nodule in noisy environment, this paper proposes an improved segmentation algorithm based on adaptive fast generalized clustering. Firstly, the parameter balance factor is adaptively determined according to the noise probability of non-local pixels so as to reflect the spatial structure information in the image more accurately. Then, the balance factor is used to effectively combine the linear weighted filtered image in the AFGC algorithm so as to create the adaptive filtered image. Since the filtering degree depends on the probability whether the pixel is noise in the image, the dynamic noise suppression performance of the proposed method can be greatly improved. A large number of qualitative and quantitative experimental results show that the proposed generalized clustering algorithm can obtain more accurate results when clustering images with high noise. It is suitable for intelligent diagnosis of papillary thyroid convolution in clinical examination.

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Authors and Affiliations

  1. The first people’s hospital of Jinshan, Jiashan, 314100, Zhejiang, China

    Weiqiang Huang

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  1. Weiqiang Huang
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Correspondence to Weiqiang Huang.

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Huang, W. Segmentation and Diagnosis of Papillary Thyroid Carcinomas Based on Generalized Clustering Algorithm in Ultrasound Elastography. J Med Syst 44, 13 (2020). https://doi.org/10.1007/s10916-019-1462-7

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