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Image segmentation is considered one of the essential steps in medical image analysis. Cases such as classification of tissue structures for quantitative analysis, reconstruction of anatomical volumes for visualization, and registration of multi-modality images for complementary study often require the segmentation of the brain to accomplish the task. In many clinical applications, parts of this task are performed either manually or interactively. Not only is this proces often tedious and time-consuming, it introduces additional external factors of inter- and intra-rater variability. In this paper, we present a 3D automated algorithm for segmenting the brain from various MR images. This algorithm consists of a sequence of pre-determined steps: First, an intensity window for initial separation of the brain volume from the background and non-brain structures is selected by using probability curves fitting on the intensity histogram. Next, a 3D isotropic volume is interpolated and an optimal threshold value is determined to construct a binary brain mask. The morphological and connectivity processes are then applied on this 3D mask for eliminating the non-brain structures. Finally, a surface extraction kernel is applied to extract the 3D brain surface. Preliminary results from the same subjects with different pulse sequences are compared with the manual segmentation. The automatically segmented brain volumes are compared with the manual results using the correlation coefficient and percentage overlay. Then the automatically detected surfaces are measured with the manual contouring in terms of RMS distance. The introduced automatic segmentation algorithm is effective on different sequences of MR data sets without any parameter tuning. It requires no user interaction so variability introduced by manual tracing or interactive thresholding can be eliminated. Currently, the introduced segmentation algorithm is applied in the automated inter- and intra-modality image registration. It will furthermore be used in different applications such as quantitative analysis of normal and abnormal brain tissues.
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