Volumetric Data Classification: A Study Direct at 3-D Imagery

This thesis describes research work undertaken in the field of image mining (particularly medical image mining). More specifically, the research work is directed at 3-D image classification according to the nature of a particular Volume Of Interest (VOI) that appears across a given image set. In this thesis the term VOI Based Image Classification (VOIBIC) has been coined to describe this process. VOIBIC entails a number of challenges. The first is the identification and isolation of the VOIs. Two segmentation algorithms are thus proposed to extract a given VOI from an image set: (i) Volume Growing and (ii) Bounding Box. The second challenge that VOIBIC poses is, once the VOI have been identified, how best to represent the VOI so that classification can be effectively and efficiently conducted. Three approaches are considered. The first is founded on the idea of using statistical metrics, the Statistical Metrics based representation. This representation offers the advantage in that it is straightforward and, although not especially novel, provides a benchmark. The second proposed representation is founded on the concept of point series (curves) describing the perimeter of a VOI, the Point Series representation. Two variations of this representation are considered: (i) Spoke based and (ii) Disc based. The third proposed representation is founded on a Frequent Subgraph Mining (FSM) technique whereby the VOI is represented using an Oct-tree structure to which FSM can be applied. The identified frequent subtrees can then be used to define a feature vector representation compatible with many classifier model generation methods. The thesis also considers augmenting the VOI data with meta data, namely age and gender, and determining the effect this has on performance. The presented evaluation used two 3-D MRI brain scan data sets: (i) Epilepsy and (ii) Musicians. The VOI in this case were the lateral ventricles, a distinctive VOI in such MRI brain scan data. For evaluation purposes two scenarios are considered, distinguishing between: (i) epilepsy patients and healthy people and (ii) musicians and non-musicians. The results indicates that the Spoke based point series representation technique produced the best results with a recorded classification accuracy of up to 78.52% for the Epilepsy dataset and 84.91% for the Musician dataset

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