Knowledge-based segmentation and labeling of brain structures from MRI images
Introduction
Magnetic resonance imaging (MRI) plays a crucial role in noninvasive in vivo study of the human brain, for its high three-dimensional spatial resolution, and its remarkable discrimination of soft tissues. Segmentation and labeling of brain neuroanatomical structures are a prerequisite for quantitative morphometric analysis, three-dimensional volume visualization, and measurement of structure–function correlationship, particularly in clinical investigations, such as pathology, diagnosis, therapy, surgery planning and guidance (Zijdenbos and Dawant, 1994, Clarke et al., 1995). As far as we are concerned, our paper is devoted to the developing of an automatic method to segment and label precisely internal brain structures, such as ventricle, caudate, thalamus and putamen, from MRI images.
Automated labeling of structures is however complicated, facing difficulties due to overlapping intensities, anatomical variability in shape, size, and orientation, partial volume effects, as well as noise perturbations, intensity inhomogeneities, and low contrast in images. Therefore, it is inevitable to supplement anatomical knowledge, to achieve labeling like what radiologists do. In recent years, many reports have been published in this direction in terms of atlas-based (model-based, or knowledge-based) segmentation of neuroanatomical structures.
One intuitive strategy to use knowledge for labeling, named as the registration–segmentation paradigm by Collins and Evans (1999), is to register and transfer labels of a pre-labeled atlas onto the MRI images to be segmented. Various registration schemes have been presented for this purpose (Collins and Evans, 1999, Collins et al., 1995, Dawant et al., 1998, Ferrant et al., 1999, Meier et al., 1998, Chen et al., 1998). The performance of this strategy overrelies on the accuracy of the registration employed, which suffers from the limited degrees of freedom of the transformations, and from anatomical variabilities (for instance, in orientation, shape, size, and position). Furthermore, a one-to-one mapping does not always exist (Collins and Evans, 1999).
Another important strategy is to integrate the statistical knowledge of intensity and position into a shape model, and to locate the structures which match the model. Staib et al. (1997) used a gradient-based parametric deformable shape model, integrating region information and prior probability knowledge of mean shape and variation of the structures. Gonzalez Ballester et al. (1998) guided the segmentation by statistical shape knowledge built from datasets of pre-labeled structures. Several researchers used active shape models (ASM), introduced by (Cootes et al., 1994) to label brain structures (Kelemen et al., 1997, Duta and Sonka, 1998). ASM are parametric deformable models of shape and appearance of flexible objects, which restrict the possible deformation using shape template and intensity model, both generated through statistics of training sets. Kelemen et al. (1997) extended ASM to three dimensions and employed parametric representations of object shapes with elliptical harmonics. Duta and Sonka (1998) reported another improvement of ASM by incorporating a priori knowledge of structures. The performance of the shape model-based strategy is spoiled by the mismatching between the geometric model and the MRI gray level data (Collins et al., 1995).
Besides the two strategies aforementioned, other methods are also reported, for instance, GAs-based (genetic algorithms) interpretation (Sonka et al., 1996), atlas-based sequential recognition using information fusion (Géraud et al., 1999) and ANN-based (artificial neural network) identification (Magnotta et al., 1999).
The contribution of this paper is twofold. First, we propose a coarse-to-fine strategy to achieve precise segmentation and labeling of brain structures, based on the structural knowledge from the Talairach stereotaxic atlas (Talairach and Tournoux, 1998), and the statistical information from the MRI images under study. The atlas-based registration is used only to indicate the coarse location of the structures in the images. GAs are applied to search for the optimal labeling of oversegmented regions, the result of which is refined through voxelwise amendment by parallel region growing. Second, we introduce a fuzzy model of regions of interest (ROI) by analogy with the electrostatic potential distribution in the vicinity of hollow structures with uniform surface charge density. We use this model to describe the spatial and geometric knowledge of structures, to estimate the statistical moments, to design the objective function of GAs, and to guide the voxelwise amendment.
The paper is organized as follows. In Section 2, we detail the proposed method, involving preprocessing, fuzzy Markov random field (MRF) based oversegmentation, coarse-to-fine labeling. The validation is implemented quantitatively in Section 3, using manually labeled MRI images as reference. In Section 4, we make the conclusion.
Section snippets
Outline
The Talairach atlas is well accepted in medical image processing, owing to its contribution to the delineation and labeling of numerous brain neuroanatomical structures. As the Talairach atlas, denoted by , is sketch-based rather than intensity-based (one sample slice of this atlas in axial direction is shown in Fig. 1(a)), no algorithm exists to automatically superimpose it onto an MRI volume. Therefore, an MRI volume , which has been registered interactively into the stereotaxic
Results and quantitative validation
In our study, the subjects were scanned with a GE Signa 1.5 Tesla scanner, employing a T1-weighted spoiled gradient recalled (SPGR) pulse sequence. The parameters of the SPGR sequence were °; each dataset (volume) consists of 256×256×124 voxels, i.e., 124 axial slices with 256×256 voxels in each slice; the size of each voxel is .
We employ six different indices of quantitative measure to validate the accuracy and reliability of this method, compared with
Conclusion
An automatic, knowledge-based method to segment and label brain neuroanatomical structures (for instance, ventricle, caudate, thalamus and putamen) from MRI images has been developed, using structural knowledge derived from the Talairach stereotaxic atlas, statistical and singularity information from the images to be labeled. To achieve a precise labeling of the desired structures, a coarse-to-fine strategy has been proposed, involving regionwise labeling using GAs followed by a voxelwise
Acknowledgements
We wish to express our gratitude to Prof. B. Mazoyer (CYCERON) for having provided the MRI data utilized in the present study. We are also thankful to Dr. J.M. Constans (CHRU de Caen) for his valuable suggestions, and to Dr. J.M. Fadili, for the three-dimensional visualization.
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