KEYWORDS: Image processing, Data fusion, Image fusion, Brain, Magnetic resonance imaging, Computed tomography, Medical imaging, 3D image processing, Data modeling, Image segmentation
Among the studies concerning the segmentation and the identification of anatomical structures from medical images, one of the major problems is the fusion of heterogeneous data for the recognition of these structures. In this domain, the fusion of inter-patient data for the constitution of anatomical models for instance is particularly critical especially with regards to the identification of complex cerebral structures like the cortical gyri. The goal of this work is to find anatomical markers which can be useful to characterize specific regions in brain images by using either CT or MR images. We have focused this study on the definition of a geometrical operator based on the detection of local extremum curvatures. The main issues addressed by this work concern the fusion of multimodal data from one patient (e.g. between CT and MRI) and moreover the fusion of inter-patient data as a first step toward the modelling of brain morphological deformations. Examples are shown upon 2D MR and CT brain images.
The new magnetic resonance imaging systems (MRI) are able to perform a brain scan with fairly good three-dimensional resolution. In order to allow the physician, and especially the neuroanatomist, to deal with the prime information borne by the images, the prevalent data have to be enhanced with regards to the medical objective. The aim of the work presented in this paper is to recognize and to label the head structures from MR images. This is done by computing probabilities for a pixel to belong to pre-specified head structures (i.e., skin, bone, CSF, ventricular system, grey and white matter, and brain). Several ways are presented and discussed in this paper, including the computation of statistical properties like `Markov parameters' and `fractal dimension.' From these statistical parameters, computed from a single MR image or a 3-D isotropic MR database, clustering and classification processes are used to issue fuzzy membership coefficients representing the probabilities for a pixel to belong to a particular structure. Improvements are proposed with regard to the expressed choices and examples are presented.
KEYWORDS: Fuzzy logic, Magnetic resonance imaging, Brain, Image segmentation, 3D displays, Image processing, Binary data, 3D image processing, Surgery, 3D magnetic resonance imaging
The overall objective in neurosurgery is to localize and to treat a target volume within the cerebral medium as well as to understand its environment. To complete this objective, the 3D display of multimodality information is required; among them CT, MRI, angiography or atlas are particularly important. During the last decade solutions have been proposed to improve the rendering of 3D CT data sets. Applied to MRI without preprocessing these methods are not able to provide a good display quality for the brain anatomy for instance. This paper presents one year of experience in the 3D display of MRI volumes, oriented to the preparation of neurosurgery procedures (e.g. biopsy, epilepsy surgery): the main issues concerning the volume anisotropy, the brain segmentation and the volume rendering are explained. Emphasis is also given to the original way we propose to solve the brain segmentation problem by using automatic segmentation techniques (fuzzy masks and region valley following). The volume rendering technique is also presented and discussed (binary segmentation vs fuzzy segmentation). Finally, examples are presented concerning the use of 3D MRI images.
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