Image segmentation is a complex mathematical problem, especially for images that contain intensity inhomogeneity and tightly packed objects with missing boundaries in between. For instance, Magnetic Resonance (MR) muscle images often contain both of these issues, making muscle segmentation especially difficult. In this paper we propose a novel intensity correction and a semi-automatic active contour based segmentation approach. The approach uses a geometric flow that incorporates a reproducing kernel Hilbert space (RKHS) edge detector and a geodesic distance penalty term from a set of markers and anti-markers. We test the proposed scheme on MR muscle segmentation and compare with some state of the art methods. To help deal with the intensity inhomogeneity in this particular kind of image, a new approach to estimate the bias field using a fat fraction image, called Prior Bias-Corrected Fuzzy C-means (PBCFCM), is introduced. Numerical experiments show that the proposed scheme leads to significantly better results than compared ones. The average dice values of the proposed method are 92.5%, 85.3%, 85.3% for quadriceps, hamstrings and other muscle groups while other approaches are at least 10% worse.

本文提出了一种新的强度校正和半自动主动轮廓分割方法，并使用了几何流，包括重现核希尔伯特空间边缘检测器和一组标记和反标记的测地距离惩罚项。通过与一些最先进的方法进行比较，并在MR肌肉分割中进行测试，该方法显示出显著更好的结果。与其他方法相比，所提出的方法的平均Dice值分别为92.5％、85.3％、85.3％，对于股四头肌、腿筋膜和其他肌肉群组，其他方法至少差10％。

一种用于具有不均匀强度和缺失边界图像分割的几何流方法