Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Semi-Supervised Approach Based Brain Tumor Detection with Noise Removal

Brain tumor detection and segmentation is the most important challenging and time consuming task in the medical field. In this paper, Magnetic Resonance Imaging (MRI) sample image is considered and it is very useful to detect the Tumor growth. It is mainly used by the radiologist for visualization process of an internal structure of the human body without any surgery. Generally, the Tumor is classified into two types such as malignant and benign. There are many variations in tumor tissue characteristics like its shape, size, gray level intensities and its locations. In this paper, we propose a new cooperative scheme that applies a semi-supervised fuzzy clustering algorithm. Specifically, the Otsu (Oral Tracheal Stylet Unit) method is used to remove the Background area from a Magnetic Resonance Image. Finally, Semi-supervised Entropy Regularized Fuzzy Clustering algorithm (SER-FCM) is applied to improve the quality level. The intensity, shape deformation, symmetry and texture features were extracted from each image. The usefulness and significance of this research are fully demonstrated within the extent of real-life application

Binary and nonbinary description of hypointensity for search and retrieval of brain MR images

Diagnosis accuracy in the medical field, is mainly affected by either lack of sufficient understanding of some diseases or the inter/intra-observer variability of the diagnoses. We believe that mining of large medical databases can help improve the current status of disease understanding and decision making. In a previous study based on binary description of hypointensity in the brain, it was shown that brain iron accumulation shape provides additional information to the shape-insensitive features, such as the total brain iron load, that are commonly used in clinics. This paper proposes a novel, nonbinary description of hypointensity in the brain based on principal component analysis. We compare the complementary and redundant information provided by the two descriptions using Kendall's rank correlation coefficient in order to better understand the individual descriptions of iron accumulation in the brain and obtain a more robust and accurate search and retrieval system