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Title: Segmentation of the liver from 3D MRI data
Author: Ibrahim, Haidi
Awarding Body: University of Surrey
Current Institution: University of Surrey
Date of Award: 2005
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Three dimensional (3D) visualisation has the potential to significantly ease the decision making in presurgical planning. The first stage of creating a 3D model for this purpose is to segment the liver from magnetic resonance (MRI) images. However, MRI images often contain data corrupted by intensity variations in held strength due to the sensitivity of the radio frequency (rf) coils used in the A/IRI scanner. In this thesis, we investigate several approaches to arrive at a solution to overcome this inhomogeneity problem, and at the same time, improve the image quality. These experiments show that the use of local enhancement, followed by median filtering, and toboggan contrast enhancement, is a good solution to achieve this aim. We then automate a segmentation technique known as intelligent scissors to segment the liver. The user only needs to select an initial slice, and the method is executed automatically. From the initial slice, the contour propagates inside the volume and segments the liver in every slice using a dynamic programming algorithm.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available