A prostate MRI segmentation method based on prior-shape-information combined with gradient and regional information

Abstract

With the increasing and younger incidence of prostate cancer, the extraction of prostate contour from magnetic resonance imaging (MRI) plays an increasingly important role in clinical diagnosis and related medical research. In order to accurately segment the prostate region in MR images, we propose a level set segmentation model, which integrates the prior shape information of the prostate and the gradient information into the region-scalable fitting (RSF) model proposed by Li et al. to achieve high-precision segmentation of the prostate. In our model, we use the Gaussian probability model to establish the statistical learning of the prior shape, expressed as the external energy term of the evolution curve, and use the local grey information and the improved gradient information as the internal energy term to construct the final level set segmentation model. This method uses the information of the image itself to modify the evolution curve under the constraints of the prior shape, which can better deal with the small difference between the background and the target area in the medical image, and improve the accuracy and robustness of the segmentation. The final experimental results and analysis illustrate the feasibility of the method used in prostate MRI segmentation.