| dc.description.abstract | Magnetic Resonance Imaging (MRI) is a useful medical instrument in medical science. It provides unparallel capability of revealing soft tissue characterization as well as 3-D visualization and proposes the diagnosis without needing to intrude into the human body. MRI produces a sequence of multiple spectral images of tissues with a variety of contrasts, but the multispectral images cannot be conveniently used to be a pathology diagnosis correctly. In general, we need to transform the multispectral images to an enhanced image, which is easier to be used for doctor’s clinical diagnosis. One of the potential applications of MRI in clinical practice is the brain parenchyma classification.
In this dissertation, we present an automatic feature selection method called Target Generation Process (TGP) and combine it with Linear Discriminant Analysis (LDA), Constrained Energy Minimization (CEM) filter and Fuzzy Knowledge Based Seeded Region Growing (FKSRG) to automatically enhance, classify and segment the three major tissues, i.e. gray matter (GM), white matter (WM) and cerebral spinal fluid (CSF), from a multispectral MR image of the human brain. The TGP is a fuzzy-set process that generates a set of potential targets from unknown information and then the targets are applied into LDA, CEM and FKSRG methods. It can support LDA and CEM to become the TGP+LDA, TGP+CEM filter. In conventional regions merging of Seeded Region Growing (SRG) algorithm, the final number of regions is unknown. Therefore, TGP is proposed and applied to support conventional regions merging, such that the FKSRG method does not produce over or under segment images. Finally, two images sets, namely, computer-generated phantom images and real MR images, are used in experiments to assess the effectiveness of the proposed methods. Experimental results demonstrate that proposed methods segment multispectral MR images much more effectively than the FMRIB’s Automated Segmentation Tool (FAST), Fuzzy C-means (FCM) and C-means (CM) methods. | en_US |