Development of Texture Weighted Fuzzy C-Means Algorithm for 3D Brain MRI Segmentation

The segmentation of human brain Magnetic Resonance Image is an essential component in the computer-aided medical image processing research. Brain is one of the fields that are attracted to Magnetic Resonance Image segmentation because of its importance to human. Many algorithms have been developed over decades for brain Magnetic Resonance Image segmentation for diagnosing diseases, such as tumors, Alzheimer, and Schizophrenia. Fuzzy C-Means algorithm is one of the practical algorithms for brain Magnetic Resonance Image segmentation. However, Intensity Non- Uniformity problem in brain Magnetic Resonance Image is still challenging to existing Fuzzy C-Means algorithm. In this paper, we propose the Texture weighted Fuzzy C-Means algorithm performed with Local Binary Patterns on Three Orthogonal Planes. By incorporating texture constraints, Texture weighted Fuzzy C-Means could take into account more global image information. The proposed algorithm is divided into following stages: Volume of Interest is extracted by 3D skull stripping in the pre-processing stage. The initial Fuzzy C-Means clustering and Local Binary Patterns on Three Orthogonal Planes feature extraction are performed to extract and classify each cluster’s features. At the last stage, Fuzzy C-Means with texture constraints refines the result of initial Fuzzy C-Means. The proposed algorithm has been implemented to evaluate the performance of segmentation result with Dice’s coefficient and Tanimoto coefficient compared with the ground truth. The results show that the proposed algorithm has the better segmentation accuracy than existing Fuzzy C-Means models for brain Magnetic Resonance Image

Automatic Focal Cortical Dysplasiav(FCD) detection by Magnetic Resonance Image (MRI)

Nowadays, approximately 50 million people are suffering from epilepsy all over the world, of whom 30% have Focal Cortical Dysplasia (FCD), a malformation that occurs during brain cortical development. In clinical treatments, FCD lesions often have to be removed by resective surgery. Magnetic Resonance Imaging (MRI) is the most important clinical tool for identifying FCD lesions, and has allowed the diagnostic detection of FCD lesions in an increasing number of patients, leading to increased rates of successful resective surgery. However, detection of FCD lesions is still a challenging task because of various factors such as extremely subtle FCD malformations, complex convolutions of human cerebral cortex and partial volume effect due to imaging. Previous works develop MRI features of FCD lesions to highlight FCD regions. However, these MRI features also exist in Healthy Controls. We developed a new MRI features of FCD lesions, and use a multi-feature based method to perform automatic FCD detection. As a results, we improve the similarity index than the previous method. Sensitivity and specificity are also improved by proposed work. The proposed work can be a useful clinical tool to assist FCD detection

Magnetic Resonance Image Processing using Levy

We consider the physical nature of the self-diffusion of water molecules in tissue and explore how (Nuclear) Magnetic Resonance (MR) imaging may be used as a means of measuring the rate of diffusion in vivo. A discussion is presented on how these techniques may be implemented as a non-invasive means of assessing the response of tumours to novel therapeutics including some of the basic advantages and disadvan- tages when compared to other methods. The physical basis and mathematical models for diffusion are considered together with models for the distribution of the diffusion co- efficient including a Lévy distributed model. Using a Lévy distributed diffusion model, we develop a novel algorithm for the purpose of improving the signal-to-noise ratio of MR images

Method of magnetic resonance image enhancement

Texaphyrins are provided for use as radiation sensitizers. Advantageous properties of texaphyrins for use as a radiation sensitizer include: i) a low redox potential which allows radiation-induced hydrated electrons to flow to texaphyrin rather than neutralizing hydroxyl radicals, allowing hydroxyl radicals to cause cellular damage, ii) a relatively stable texaphyrin radical that reacts readily to covalently modify neighboring molecules causing further cellular damage, iii) intrinsic biolocalization, and iv) indifference to the presence or absence of O.sub.2. These properties allow texaphyrins to be particularly effective for treating the hypoxic areas of solid neoplasms. Methods of treatment for an individual having a neoplasm or atheroma include the use of a texaphyrin as a radiation sensitizer and as an agent for photodynamic tumor therapy, or the use of a texaphyrin for internal and for external ionizing radiation. Novel texaphyrins are provided.Board of Regents, University of Texas Syste

Quantitative magnetic resonance image analysis via the EM algorithm with stochastic variation

Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight into pathological and physiological alterations of living tissue, with the help of which researchers hope to predict (local) therapeutic efficacy early and determine optimal treatment schedule. However, the analysis of qMRI has been limited to ad-hoc heuristic methods. Our research provides a powerful statistical framework for image analysis and sheds light on future localized adaptive treatment regimes tailored to the individual's response. We assume in an imperfect world we only observe a blurred and noisy version of the underlying pathological/physiological changes via qMRI, due to measurement errors or unpredictable influences. We use a hidden Markov random field to model the spatial dependence in the data and develop a maximum likelihood approach via the Expectation--Maximization algorithm with stochastic variation. An important improvement over previous work is the assessment of variability in parameter estimation, which is the valid basis for statistical inference. More importantly, we focus on the expected changes rather than image segmentation. Our research has shown that the approach is powerful in both simulation studies and on a real dataset, while quite robust in the presence of some model assumption violations.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS157 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org