Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.

Volume and shape in feature space on adaptive FCM in MRI segmentation.

Intensity non-uniformity (bias field) correction, contextual constraints over spatial intensity distribution and non-spherical cluster's shape in the feature space are incorporated into the fuzzy c-means (FCM) for segmentation of three-dimensional multi-spectral MR images. The bias field is modeled by a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of either intensity or membership are added into the FCM cost functions. Since the feature space is not isotropic, distance measures, other than the Euclidean distance, are used to account for the shape and volumetric effects of clusters in the feature space. The performance of segmentation is improved by combining the adaptive FCM scheme with the criteria used in Gustafson-Kessel (G-K) and Gath-Geva (G-G) algorithms through the inclusion of the cluster scatter measure. The performance of this integrated approach is quantitatively evaluated on normal MR brain images using the similarity measures. The improvement in the quality of segmentation obtained with our method is also demonstrated by comparing our results with those produced by FSL (FMRIB Software Library), a software package that is commonly used for tissue classification.

Molecular basis of Corynebacterium diphtheriae virulence and infection in the Caenorhabditis elegans model host

Corynebacterium diphtheriae is the causative agent of cutaneous and pharyngeal diphtheria in humans. While lethality is certainly caused by diphtheria toxin, corynebacterial colonization may primarily require proteinaceous fibers called pili, which mediate adherence to specific tissues. The type strain of C. diphtheriae possesses three distinct pilus structures, namely the SpaA, SpaD, and SpaH-type pili, which are encoded by three distinct pilus gene clusters. The pilus is assembled onto the bacterial peptidoglycan by a specific transpeptidase enzyme called sortase. Although the SpaA pili are shown to be specific for pharyngeal cells in vitro, little is known about functions of the three pili in bacterial pathogenesis. This is mainly due to lack of in vivo models of corynebacterial infection. As an alternative to mouse models as mice do not have functional receptors for diphtheria toxin, in this study I use Caenorhabditis elegans as a model host for C. diphtheriae. A simple C. elegans model would be beneficial in determining the specific role of each pilus-type and the literature suggests that C. elegans infection model can be used to study a variety of bacterial species giving insight into bacterial virulence and host-pathogen interactions. My study examines the hypothesis that pili and toxin are major virulent determinants of C. diphtheriae in the C. elegans model host.