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.
Subject(s)
Diffusion Magnetic Resonance Imaging/methods , Magnetic Resonance Imaging/methods , Prostatic Neoplasms/diagnosis , Prostatic Neoplasms/pathology , Algorithms , Automation , Biopsy , Electronic Data Processing , Humans , Image Processing, Computer-Assisted/methods , Male , Models, Statistical , Pattern Recognition, Automated , Probability , Reproducibility of ResultsABSTRACT
Perspective projections in the space-frequency plane are analyzed, and it is shown that under certain conditions they can be approximately modeled in terms of the fractional Fourier transform. The region of validity of the approximation is examined. Numerical examples are presented.
