Abstract
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.