Medical imaging plays a vital role in the medical field because it is widely
used in diseases diagnosis and treatment of patients. There are different
modalities of medical imaging such as ultrasounds, x-rays, Computed Tomography
(CT), Magnetic Resonance (MR), and Positron Emission Tomography
(PET). Most of these modalities usually suffer from noise and other sampling
artifacts. The diagnosis process in these modalities depends mainly on the
interpretation of the radiologists. Consequently, the diagnosis is subjective
as it is based on the radiologist experience.
Medical image segmentation is an important process in the field of image
processing. It has a significant role in many applications such as diagnosis,
therapy planning, and advanced surgeries. There are many segmentation
techniques to be applied on medical images. However, most of these
techniques are still depending on the experts, especially for initializing the
segmentation process. The artifacts of images can affect the segmentation
output.
In this thesis, we propose a new approach for automatic prostate segmentation
of Trans-Rectal UltraSound (TRUS) images by dealing with the
speckle not as noise but as informative signals. The new approach is an
automation of the conventional region growing technique. The proposed
approach overcomes the requirement of manually selecting a seed point for initializing the segmentation process. In addition, the proposed approach
depends on unique features such as the intensity and the spatial Euclidean
distance to overcome the effect of the speckle noise of the images. The experimental
results of the proposed approach show that it is fast and accurate.
Moreover, it performs well on the ultrasound images, which has the common
problem of the speckle noise.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/4203 |
| Date | 05 January 2009 |
| Creators | Wahba, Marian |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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