Local contextual information and Gaussian function induced fuzzy clustering algorithm for brain MR image segmentation and intensity inhomogeneity estimation - Mahata, Nabanita; Kahali, Sayan; Adhikari, Sudip Kumar; Sing, Jamuna Kanta (Applied soft computing, 2018)
•A Fuzzy clustering algorithm using local contextual information and Gaussian function is devised for bias field and brain MR image segmentation.•The algorithm works directly on the MR signal model without transforming it into a log-transformed domain.•We have used Gaussian surface to compensate the effect of bias field and the local contextual information for final labeling of pixels.•We have introduced global and local membership functions for each pixel to define its belongingness into a tissue region.•Simulation results on real-patient and simulated brain MR images demonstrate its effectiveness and superiority over other similar methods. This paper presents a fuzzy clustering algorithm, where local contextual information and a Gaussian function are incorporated into the objective function, for simultaneous brain MR image segmentation and intensity inhomogeneity estimation. In doing so, for each pixel, we define a local contextual information, which actually defines its association among the other neighboring pixels based on intensity distribution. In particular, this information defines the possibility of the pixel to belong into a specific tissue type. Whereas, for each tissue region, a Gaussian surface is defined to estimate the intensity inhomogeneity (IIH) using the local image gradients, which are believed to be caused by the IIH. We use this Gaussian surface to compensate the effect of IIH. In addition, for each pixel, we have introduced global and local membership functions, which in combined in association with the other parameters are responsible for generation of cluster prototypes. The IIH of the entire image region is iteratively removed from the image and the final segmentation result is obtained based on the global membership values. The simulation results on two benchmarks brain MR image databases and four volumes of real-patient brain MR image data show its efficiency and superiority over other fuzzy-based clustering algorithms.