A reinforcement agent for object segmentation in ultrasound images

Farhang Sahba, Hamid R. Tizhoosh, Magdy M.M.A. Salama

Research output: Contribution to journalArticlepeer-review


The principal contribution of this work is to design a general framework for an intelligent system to extract one object of interest from ultrasound images. This system is based on reinforcement learning. The input image is divided into several sub-images, and the proposed system finds the appropriate local values for each of them so that it can extract the object of interest. The agent uses some images and their ground-truth (manually segmented) version to learn from. A reward function is employed to measure the similarities between the output and the manually segmented images, and to provide feedback to the agent. The information obtained can be used as valuable knowledge stored in the Q-matrix. The agent can then use this knowledge for new input images. The experimental results for prostate segmentation in trans-rectal ultrasound images show high potential of this approach in the field of ultrasound image segmentation.

Original languageEnglish (US)
Pages (from-to)772-780
Number of pages9
JournalExpert Systems with Applications
Issue number3
StatePublished - Oct 2008


  • Image segmentation
  • Reinforcement learning
  • Ultrasound imaging

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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