M.Ing. / Automatic classification of images has always been an important part of pattern recognition. The segmentation and classification of MRI images has always been a challenge. A segmented image is often a very important input to the classification process. Many classification techniques use segmented images as input to the classification process. Certain segments or areas of an image serve as important features that will be used for classification. Important information can be derived from the features that are present in the segmented image. Sometimes there might be a need to extract a certain object from an image to do classification on the object. In the case of MRI images, certain structures of the human body like organs and tissue can be isolated by the segmentation process. These objects of interest (001) can give vital information for the identification of medical abnormalities (anomalies) and diseases. Segmented objects can play an important role to assist medical practitioners in the diagnosis and treatment of medical problems. I would like to test the performance of the watershed segmentation algorithm on MRI images of the cervical (C) spine. Much work has been done on the segmentation and classification of MRI images. Various techniques have been generated and tested over the past decades. Segmentation techniques like thresholding, convolution, pyramid segmentation and morphological segmentation have been utilised. All these techniques have their advantages and disadvantages. The pre-processing of an image plays a very important role in the success of the segmentation process. Histogram manipulation, filtering, thresholding and edge detection are important pre-processing techniques to yield good segmentation results. Many segmentation and classification techniques have been implemented on MRI images. The latest techniques include support vector machines (SVMs), neural networks (NNs), statistical methods, threshold techniques and normalised cuts. Segmentation of bony structures plays an important role in image guided surgery of the spine [1]. Physicians have commonly relied on computed tomography (CT) images to support their decisions in the diagnosis, treatment, and surgery of different pathologies of the spine due to the high resolution and good visualization of bone offered by this medical imaging modality. CT relies on the use of ionizing radiation, and does not depict soft tissue pathology, unlike magnetic resonance imaging (MRI) [1]. While the segmentation of vertebral bodies from CT images Segmentation Of C-Spine MRI Images Using The Watershed Transform Page 6 University of Johannesburg of the spine has commonly been accomplished with seed growing segmentation techniques [1], this task is more difficult in MRI, with variations in soft tissue contrast, and with the RF inhomogeneities, which increase the level of complexity. The primary goal of this project is to develop segmentation techniques for C-spine MRI images. This method will also be compared against other methods like pyramid segmentation and morphological segmentation. The watershed segmentation will be implemented and tested as the final step of the segmentation process. This project will try to use a combination of techniques, rather than to implement and evaluate one single method. It has been learned from literature and also from experience that the pre-processing of the raw data plays a crucial role in the quality of the segmentation process. Therefore, some attention will be given to the pre-processing of the images as part of the segmentation process.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uj/uj:9406 |
| Date | 15 August 2012 |
| Creators | Botha, Jacobus Johannes |
| Source Sets | South African National ETD Portal |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0023 seconds