Abstract
A Novel Wavelet Transformation-Based Detection of Masses in digital mammograms (WTBDM) is proposed in this paper that enables for the early prognosis of breast cancer. The wavelet analysis is explored for analyzing and identifying strong variations in intensities within the mammographic data which highlights and recognizes the masses effectively. The proposed algorithm, in addition to wavelet transformation, uses morphological preprocessing, region properties and seeded region growing to remove the digitization noises, to remove the pectoral muscle and to suppress radiopaque artifacts, thus segmenting the abnormal masses accurately. The combined potential of wavelet and region growing helps for effective mass segmentation that vouches the merit of the proposed technique.
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