SHIRAZ: an automated histology image annotation system for zebrafish phenomics

Histological characterization is used in clinical and research contexts as a highly sensitive method for detecting the morphological features of disease and abnormal gene function. Histology has recently been accepted as a phenotyping method for the forthcoming Zebrafish Phenome Project, a large-scale community effort to characterize the morphological, physiological, and behavioral phenotypes resulting from the mutations in all known genes in the zebrafish genome. In support of this project, we present a novel content-based image retrieval system for the automated annotation of images containing histological abnormalities in the developing eye of the larval zebrafish

Correction of distortions in MR Echo Planar images using a super-resolution T2-Weighted volume

Magnetic resonance imaging (MRI) is a widely used technique to assess brain diseases without the use of ionizing radiations. Brain anatomy can be captured using T1-Weighted (T1W) and T2-Weighted (T2W) acquisitions. In addition to mapping brain anatomy, MRI can be also applied to study the brain functions through a process called the hemodynamic response. Blood releases oxygen to neurons at a greater rate than to inactive neurons: this causes a change of the relative levels of oxygenated and deoxygenated blood, i.e. a change of the contrast between the two level of blood oxygenation that can be detected on the basis of their differential magnetic susceptibility. This acquisition technique is called functional Magnetic Resonance Imaging (fMRI), and it represents an indirect measure of the neuron activity. Although BOLD-based techniques have been shown to work reliably for a huge range of applications, straight-forward BOLD imaging has some inherent problems (such as macroscopic field inhomogeneity effects that produce spatial distortions in the acquisitions). The aim of this thesis is to give an overview of the fMRI data analysis focusing on some aspects of the preprocessing pipeline. In chapter 1 we will introduce the problem of Echo Planar Imaging (EPI) spatial distortions and a new method to correct them, based on non-linear registrations to an intra-subject T2W volume. In chapter 2 we will show the procedure for the construction of a good reference to apply the EPI-distortions correction method. This method belongs to the super-resolution algorithms and it aims to produce a T2W high resolution reference. In chapter 3, the previous methods will be combined together to perform the EPI distortion correction method. Finally, in chapter 4 we will present a bunch of clinical fMRI studies where the correction method was performed. Our results provide a good evidence of the effectiveness of the combined approach, which gives the advantage of using only standard acquisition protocol to have alle the information required to perform the proposed EPI-distortion correction

Image magnification based on a blockwise adaptive Markov random field model

In this paper, an effective image magnification algorithm based on an adaptive Markov random field (MRF) model with a Bayesian framework is proposed. A low-resolution (LR) image is first magnified to form a high-resolution (HR) image using a fractal-based method, namely the multiple partitioned iterated function system (MPIFS). The quality of this magnified HR image is then improved by means of a block-wise adaptive MRF model using the Bayesian 'maximum a posteriori' (MAP) approach. We propose an efficient parameter estimation method for the MRF model such that the staircase artifact will be reduced in the HR image. Experimental results show that, when compared to the conventional MRF model, which uses a fixed set of parameters for a whole image, our algorithm can provide a magnified image with the well-preserved edges and texture, and can achieve a better PSNR and visual quality. (C) 2008 Elsevier B.V. All rights reserved