Automatic and accurate methods to estimate normalized regional brain volumes from MRI data are valuable tools which may\nhelp to obtain an objective diagnosis and followup of many neurological diseases. To estimate such regional brain volumes, the\nintracranial cavity volume (ICV) is often used for normalization. However, the high variability of brain shape and size due to\nnormal intersubject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV\nestimation problem challenging. In this paper, we present a new approach to performICV extraction based on the use of a library of\nprelabeled brain images to capture the large variability of brain shapes. To this end, an improved nonlocal label fusion scheme based\non BEaST technique is proposed to increase the accuracy of the ICV estimation. The proposed method is compared with recent\nstate-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while\nmaintaining a reduced computational burden.
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