Combining image segmentation based on statistical classification with a geometric prior has been shown to significantly increase robustness and reproducibility. Using a probabilistic geometric model of sought structures and image registration serves both initialization of probability density functions and definition of spatial constraints. A strong spatial prior, however, prevents segmentation of structures that are not part of the model. In practical applications, we encounter either the presentation of new objects that cannot be modeled with a spatial prior or regional intensity changes of existing structures not explained by the model. Our driving application is the segmentation of brain tissue and tumors from three-dimensional magnetic resonance imaging (MRI). Our goal is a high-quality segmentation of healthy tissue and a precise delineation of tumor boundaries. We present an extension to an existing expectation maximization (EM) segmentation algorithm that modifies a probabilistic brain atlas with an individual subject's information about tumor location obtained from subtraction of post-and pre-contrast MRI. The new method handles various types of pathology, spaceoccupying mass tumors and infiltrating changes like edema. Preliminary results on five cases presenting tumor types with very different characteristics demonstrate the potential of the new technique for clinical routine use for planning and monitoring in neurosurgery, radiation oncology, and radiology. The segmentation of medical images, as opposed to natural scenes, has the significant advantage that structural and intensity characteristics are well known up to a natural biological variability or the presence of pathology. Most common is pixel-or voxel-based statistical classification using multiparameter images A geometric prior can be used by atlas-based segmentation, which regards segmentation as a registration problem in which a fully labeled, template MR volume is registered to an unknown dataset. High-dimensional warping results in a one-to-one correspondence between the template and subject images, resulting in a new, automatic segmentation. These methods require elastic registration of images to account for geometrical distortions produced by pathological processes. Such registration remains challenging and is not yet solved for the general case. Warfield et al. [12] , [13] combined elastic atlas registration with statistical classification. Elastic registration of a brain atlas helped to mask the brain from surrounding structures. A further step uses "distance from brain boundary" as an additional feature to improve separation of clusters in multi-dimensional feature space. Initialization of probability density functions still requires a supervised selection of training regions. The core idea, namely to augment statistical classification with spatial information to account for the overlap of distributions in intensity feature space, is part of the new method presented in this paper. Leemput et al. [14], [15] developed automatic segmentation of MR images of normal brains by statistical classification, using an atlas prior for initialization and also for geometric constraints. A most recent extension detects brain lesions as outliers [16] and was successfully applied for detection of multiple sclerosis lesions. Brain tumors, however, can't be simply modeled as intensity outliers due to overlapping intensities with normal tissue and/or significant size. We propose a fully automatic method for segmenting MR images presenting tumor and edema, both mass-effect and infiltrating structures. This method builds on the previously published work done by [14] , [15] . Additionally, tumor and edema classes are added to the segmentation. The spatial atlas that is used as a prior in the classification is modified to include prior probabilities for tumor and edema. As with the work done by other groups, we focus on a subset of tumors to make the problem tractable. Our method provides a full classification of brain tissue into white matter, grey matter, cerebrospinal fluid (csf), tumor, and edema. Because the method is fully automatic, its reliability is optimal.
Moon et al. REF introduced an atlas-based segmentation technique.
Automatic brain and tumor segmentation
Medical Image Computing and Computer-Assisted Intervention MICCAI 2002. Volume 2489 of LNCS
Computer Science