Analysis of Heterokaryon Formation in Fusarium oxysporum f. sp. cubense.

Fusarium oxysporum forma specialis cubense is a soilborne phytopathogen that infects banana. The true evolutionary identity of this so called species, Fusarium oxysporum, is still unknown. Many techniques have been applied in order to gain insight for the observed genetic diversity of this species. The current classification system is based on vegetative compatibility groups (VCG's). Vegetative compatibility is a self non-self recognition system in which only those belonging to a VCG can form stable heterokaryons, cells containing two distinct nuclei. Heterokaryons in turn, are formed from hypha! anastomosis, the fusion of two hyphae. Furthermore, subsequent to heterokaryon formation potential mechanisms exist which may generate genetic variability. One is through viral transfer upon hyphal anastomosis. The other mechanism is a form of mitotic recombination referred to as the parasexual cycle. Very little research has been performed to directly obser.ve the cellular events; hypha! anastomosis, heterokaryon formation, and the parasexual cycle in Fusarium oxysporum f. sp. cubense. The purpose of this research was to design and use methods which would allow for the detection of hypha! anastomosis and heterokaryon formation, as well as any characteristics surrounding this event, within and between VCG's in Foe. First, some general growth properties were recorded: the number of nuclei per hypha, the size ofthe hyphal tip cell, the size of the cell adjacent to the hypha! tip (pre-tip) cell, and the number of cells to the first branch point. Second, four methods were designed in order to assay hyphal anastomosis and heterokaryon formation: 1) pairings on membrane: phase or brightfield microscopy, 2) pairings on membrane: fluorescence microscopy, 3) spore crosses: fluorescence microscopy, and 4) double picks in fractionated MMA. All of these methods were promtsmg.

Rigid and non-rigid point-based medical image registration

The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.

Rigid and Non-rigid Point-based Medical Image Registration

The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.