Automatic 3D Segmentation of MRI Brain Images Based on Fuzzy Connectedness

An automatic 3D segmentation based on fuzzy connectedness (FQ) is proposed for MRI brain images. The main contribution of the present paper includes two parts: the accurate extraction of brain tissues and the automatic selection of the seed of FC. The brain tissues are extracted through obtaining approximate region of brain tissues via improved region growing, choosing the optimal threshold value to separate the background and brain tissues, eroding to disconnect duramater from the region of brain tissues, and blurring brain tissues with a smoothing linear filter to obtain the template of brain tissues. For automatic selection of the seed, according to the largest gray probability density of white matter and grey matter respectively corresponding to different gray-scale values, approximate regions of white matter and grey matter are estimated; in such approximate regions, the probability density of the volume data and the intensity uniformity are utilized to select the seed automatically. This method requires no user interaction, and is fully automatic and robust. The experimental results show that the proposed method can accurately select seeds and get accurate segmentation results.