title: Unsupervised segmentation of the prostate using MR images based on level set with a shape prior.
abstract: 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 ellipse is used to initiate the level set and constrain the level set evolution with a shape penalty term. Finally, certain post processing methods are applied to refine the prostate boundaries. We apply the proposed method to real diffusion-weighted (DWI) MRI images data to test the performance. Our results show that accurate segmentation can be obtained with the proposed method compared to human readers.
authors: Medical Imaging Research Center, Illinois Institute of Technology, Chicago, IL, USA.
authors: Xin
authors: X
authors: Liu
authors: D L
authors: DL
authors: Langer
authors: M A
authors: MA
authors: Haider
authors: T H
authors: TH
authors: Van der Kwast
authors: A J
authors: AJ
authors: Evans
authors: M N
authors: MN
authors: Wernick
authors: I S
authors: IS
authors: Yetik
mesh_terms: D000465:Algorithms; D001331:Automation; D001706:Biopsy; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D001330:Electronic Data Processing; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011336:Probability; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D015203:Reproducibility of Results