Wavelet Transformation-Based Detection of Masses in Digital Mammograms

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.

Image Processing For Identification of Breast Cancer: A Literature Survey

“…Therefore, a new algorithm and set of filters (kernels) is proposed and its results are compared with the Sobel and Laplacian filters for three images. From the results obtained it is found that the proposed algorithm performs better than in terms of less false edges than the Sobel and Laplacian filters ShanmugaVadivu and Sivakumar [28] Wavelet Transformation-Based Detection of…”