| Summary: | Thesis (M.Sc.)--Memorial University of Newfoundland, 2011. Medicine Bibliography: leaves 114-122. One of the best ways to decrease breast cancer mortality is through early detection. X-ray mammography is widely used to screen women with an increased risk of breast cancer. Computer aided detection (CAD) programs have been developed in an effort to boost efficiency and accuracy, but studies have shown that the CAD programs currently in use are not particularly effective. -- In this project, a new CAD algorithm was developed. The two main components of the method were the use of whole image classification and a novel feature extraction step using the discrete cosine transform. The features were generated from moments of the mean of square sections centered on the origin of the transform. Feature vectors were then run through k-nearest neighbour and naive Bayesian classifiers. -- It was found that the discrete cosine transform could be used to manually filter suspicious characteristics from images. Features extracted from the images were found to change dramatically when a mass was introduced into the image. Using a k-nearest neighbour classifier, sensitivities as high as 98% with a specificity of 66% was achieved. With a naive Bayesian classifier, sensitivities as high as 100% were achieved with a specificity of 64%.
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