A Geometric Flow Approach for Segmentation of Images with Inhomongeneous Intensity and Missing Boundaries
This addresses the problem of accurate muscle segmentation in medical imaging for clinicians, but it is incremental as it builds on existing active contour and bias correction techniques.
The paper tackles muscle segmentation in MR images with intensity inhomogeneity and missing boundaries by proposing a novel intensity correction and semi-automatic active contour method, achieving average dice values of 92.5%, 85.3%, and 85.3% for different muscle groups, which are at least 10% better than state-of-the-art methods.
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.