Unsupervised segmentation of the prostate using MR images based on level set with a shape prior

@article{Liu2009UnsupervisedSO,
  title={Unsupervised segmentation of the prostate using MR images based on level set with a shape prior},
  author={Xin Liu and Deanna L. Langer and M. A. Haider and Theodorus H van der Kwast and Andrew J. Evans and M. N. Wernick and Imam Samil Yetik},
  journal={2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
  year={2009},
  pages={3613-3616}
}
Prostate cancer is the second leading cause of cancer death in American men. Current prostate MRI can benefit from automated tumor localization to help guide biopsy, radiotherapy and surgical planning. An important step of automated prostate cancer localization is the segmentation of the prostate. In this paper, we propose a fully automatic method for the segmentation of the prostate. We firstly apply a deformable ellipse model to find an ellipse that best fits the prostate shape. Then, this… CONTINUE READING

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