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Image processing on medical application - Automatic methods to calculate the area of articular cartilage on a magnetic resonance image


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Ngo, QL (2011) Image processing on medical application - Automatic methods to calculate the area of articular cartilage on a magnetic resonance image. Research Master thesis, University of Tasmania.

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Digital image processing plays a more and more important role in the medical
diagnoses. Most widely used method for visualizing internal anatomical features of
human body is magnetic resonance imaging (MRI). MRI can be used for the detection
of osteoarthritis (OA) which affects the cartilage in the joints. Several techniques for
the segmentation of the cartilage in MRI scans of the knee have recently been
developed. One goal of segmentation is to automatically determine area and volume of
the cartilage. Due to noise, however, none of these approaches is satisfactory in fully
automated segmentation of articular cartilage.
In our research, we attempt to study and develop new automatic methods to extract
the cartilage from MRI knee scans. From cartilage images, cartilage area and volume
are then computed. Three automatic methods are applied for cartilage extraction.
The first method is bi-directional scanning segmentations method (BSSM), which is
based on two basic properties of intensity value: discontinuity (edge detection
algorithms) and similarity (thresholding algorithms). It is also based on statistical
analysis (curve fitting algorithms and average weight calculation).
The second method is neural network classifier method (NNCM), which is based on
artificial neural network. For each pixel on an input image, through a neural network
classifier, it is classified as a cartilage pixel if network output value is 1. Alternatively,
it is classified as a background pixel if network output value is 0.
The third method is active contour models method (ACMM), which uses an initial
contour that approximates the boundary of a cartilage to find the “actual” boundary.
This is an innovative method because we apply BSSM to define the initial contour. We
also apply NNCM to compute the external energy in active contour models algorithms.
Our three methods have succeeded in automatically extracting the cartilage from
input image. The cartilage area and volume obtained from cartilage image, which is
extracted by BSSM, NNCM, and ACMM, are highly correlation with the results
obtained from reference cartilage image (correlation value in each case p ≈ 1, R2 ≈ 1).
Thus, cartilage area and volume assessments are precise, reliable and acceptable.
Among those methods, the ACMM provides the best results. Considering noise and
complexity of the image, each method has both advantages and disadvantages. BSSM
work well where there is significant high contrast between cartilage and background regions. It often fails where the contrast is low. NNCM can work well in low contrast.
However, this method does not work accurately if the cartilage pixels have similar
features of background pixels. Those pixels are classified as background pixels. As a
result, this method reduces number of cartilage pixels. The third method, ACMM
demonstrates higher accuracy in extracting cartilage from an input image. ACMM not
only can take advantages of BSSM and NNCM but also can solve the problems
existing in BSSM and NNCM. Therefore, results obtained from ACMM are the most
precise and acceptable.

Item Type: Thesis (Research Master)
Keywords: Image processing, intelligent system, articular cartilage, magnetic resonance image, volume, area
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Date Deposited: 12 Dec 2011 22:34
Last Modified: 11 Mar 2016 05:53
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