Mammograms, which are X-ray images of the female breast, are used widely by radiologists to screen for breast cancer. The first stage of any computerized analysis of the digitised mammogram is to divide the image into anatomically distinct regions. The pectoral muscle is one of these regions and it appears on mediolateral oblique views of mammograms. In this paper, the rationale and algorithms for fully automatic, two-part segmentation of the pectoral muscle are presented. The algorithm consists of (a) estimation of the muscle edge by a straight line; and (b) refinement of the detected edge by surface smoothing and edge detection in a restricted neighbourhood derived from the first estimate.

Daugman and Downing introduced a new method in 1993 for decomposing textures using a demodulation transform that they claimed had its basis in human visual perception. We argue that their transfomi may be applied to mammograms for image texture analysis and tissue characterization. In this paper, an adaptation made to their demodulation transform is presented for the purpose of highlighting circumscribed mass lesions on mammograms. Three major modifications are introduced: (a) Fourier half-plane selection, (b) amplitude-only and phase-only reconstruction, and (c) image subtraction to highlight masses on mammograms. This adapted algorithm exhibited high sensitivity for detecting -~ circumscribed mass lesions and initial results are promising

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