A neural network for thyroid segmentation and volume estimation in CT images

Chuan Yu Chang, Pau Choo Chung, Yong Cheng Hong, Chin Hsiao Tseng

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


Thyroid region segmentation and volume estimation is a prerequisite step to diagnosing the pathology of the thyroid gland. In this study, a progressive learning vector quantization neural network (PLVQNN) combined with a preprocessing procedure is proposed for automatic thyroid segmentation and volume estimation using computerized tomography (CT) images. The preprocessing procedure is used to extract the region of interest (ROI) of thyroid glands and exclude non-thyroid glands based on thyroid anatomy. The PLVQNN contains several learning vector quantization neural networks (LVQNNs), each responsible for segmenting one slice of a thyroid CT image. The training of the PLVQNN is conducted starting from the LVQNN of most reliable (middle) slices, where the thyroid has the largest region. The training then propagates upwards and downwards to adjacent LVQNNs using the results of the middle slice as the initialization values and constraints. Experimental results show that the proposed method can effectively segment thyroid glands and estimate thyroid volume.

Original languageEnglish
Article number6052365
Pages (from-to)43-55
Number of pages13
JournalIEEE Computational Intelligence Magazine
Issue number4
Publication statusPublished - 2011 Nov 1

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Artificial Intelligence

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