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Seminar: Improving Specificity in Mammography Using Cross-correlation between Wavelet and Fourier Transform
Paul Price
Zhang, Liuhua

M.Sc. Candidate

Co-Supervisors: Dr. Adrian Fiech and Dr. Edward Kendall



Department of Computer Science

Wednesday, October 3, 2012, 12:00 p.m., Room EN 2022





Abstract



Breast cancer is in the most common malignant tumor in women. It accounted for

30% of new malignant tumor cases. Although the incidence of breast cancer

remains high around the world, the mortality rate has been continuously

reduced. This is mainly due to recent developments in molecular biology

technology and improved level of comprehensive diagnosis and standard

treatment. Early detection by mammography is an integral part of that.



The most common breast abnormalities that may indicate breast cancer are masses

and calcifications. Previous detection approaches usually obtain relatively

high sensitivity but unsatisfactory specificity. We will investigate an

approach that applies the discrete wavelet transform and Fourier transform to

parse the images and extracts statistical features that characterize an image’s

content, such as the mean intensity and the skewness of the intensity. A naïve

Bayesian classifier uses these features to classify the images. We expect to

achieve an optimal high specificity.

Oct 2nd, 2012

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