KEYWORDS: Tissues, Brain, Image segmentation, Chemical elements, Visualization, Medical imaging, Magnetic resonance imaging, Statistical modeling, Data acquisition, Brain mapping
Tissue abnormality characterization is a generalized segmentation problem which aims at determining a continuous
score that can be assigned to the tissue which characterizes the extent of tissue deterioration, with completely
healthy tissue being one end of the spectrum and fully abnormal tissue such as lesions, being on the other end.
Our method is based on the assumptions that there is some tissue that is neither fully healthy or nor completely
abnormal but lies in between the two in terms of abnormality; and that the voxel-wise score of tissue abnormality
lies on a spatially and temporally smooth manifold of abnormality. Unlike in a pure classification problem
which associates an independent label with each voxel without considering correlation with neighbors, or an
absolute clustering problem which does not consider a priori knowledge of tissue type, we assume that diseased
and healthy tissue lie on a manifold that encompasses the healthy tissue and diseased tissue, stretching from
one to the other. We propose a semi-supervised method for determining such as abnormality manifold, using
multi-parametric features incorporated into a support vector machine framework in combination with manifold
regularization. We apply the framework towards the characterization of tissue abnormality to brains of multiple
sclerosis patients.
The motivation of this work is to register MR brain tumor images with a brain atlas. Such a registration method can
make possible the pooling of data from different brain tumor patients into a common stereotaxic space, thereby enabling
the construction of statistical brain tumor atlases. Moreover, it allows the mapping of neuroanatomical brain atlases into
the patient's space, for segmenting brains and thus facilitating surgical or radiotherapy treatment planning. However, the
methods developed for registration of normal brain images are not directly applicable to the registration of a normal atlas
with a tumor-bearing image, due to substantial dissimilarity and lack of equivalent image content between the two
images, as well as severe deformation or shift of anatomical structures around the tumor. Accordingly, a model that can
simulate brain tissue death and deformation induced by the tumor is considered to facilitate the registration. Such tumor
growth simulation models are usually initialized by placing a small seed in the normal atlas. The shape, size and location
of the initial seed are critical for achieving topological equivalence between the atlas and patient's images. In this study,
we focus on the automatic estimation of these parameters, pertaining to tumor simulation. In particular, we propose an
objective function reflecting feature-based similarity and elastic stretching energy and optimize it with APPSPACK
(Asynchronous Parallel Pattern Search), for achieving significant reduction of the computational cost. The results
indicate that the registration accuracy is high in areas around the tumor, as well as in the healthy portion of the brain.
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