Hidden Markov model-based extraction of target objects in X-ray image sequence for lung radiation therapy

Masahiro Shindo, Kei Ichiji, Noriyasu Homma, Xiaoyong Zhang, Shungo Okuda, Norihiro Sugita, Shunsuke Yamaki, Yoshihiro Takai, Makoto Yoshizawa

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


It is an important task to accurately track the target tumor with respiratory movement during radiation therapy. X-ray imaging technique is capable of observing the internal organ motion. However, superimposed tissues and structures in X-ray images decrease tumor localization accuracy. This paper presents a target extraction method based on hidden Markov model (HMM) to enhance the target tumor in X-ray images for improving the tumor tracking accuracy. We first simulate possible combinations of image intensities of target objects as hidden states and observable X-ray image intensities as output symbol in HMM by using digitally reconstructed radiographs generated from four-dimensional X-ray computed tomography. Subsequently, the transition dynamics of the hidden states and output symbols is estimated by applying Baum-Welch algorithm to a training dataset. The transition sequence of the hidden states is inversely estimated from the observed X-ray image sequence by using Viterbi algorithm, and then the transition sequence is finally decomposed into the subset image sequences. Experimental results demonstrated that tracking performance of the proposed method is superior to that of conventional tumor enhancement method and raw images. Therefore, the proposed method has potential for contributing to effectively observe internal organ motion.

Original languageEnglish
Pages (from-to)49-60
Number of pages12
JournalIEEJ Transactions on Electronics, Information and Systems
Issue number1
Publication statusPublished - 2020


  • Hidden Markov model
  • Radiation therapy
  • Superimposition of image intensity
  • Target image extraction
  • Tumor tracking
  • X-ray fluoroscopy

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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