Location of mammograms ROI's and reduction of false-positive

There are many work related with segmentation techniques, including nearest neighbor algorithm, fuzzy rules, morphological filters, image entropy, thresholding, machine learning, wavelet analysis, and so on. Such methods carry out the segmentation, but take a lot of processing time by modifying the content of the image or showing discern problems in homogeneous areas, and the segmentation technique is designed to work efficiently only with the techniques used. In this paper a method to segment mammograms in order to separate breast area from pectoral-muscle avoiding bright areas that produce noise and therefore reducing false-positives is presented.

The proposed methodology is divided into four sections: 1) Pre-processing to acquire image and decreasing its size. 2) Improving the image quality through image thresholding and histogram equalization. 3) Localization of regions of interest (ROI) applying Scale-Invariant Feature Transform to find image's descriptors. Clustering methods were implemented to determine the best number of clusters and which of these represent the most significant breast area. Then found ROI's coordinates are compared with the position of abnormalities diagnosed by the Mammographic Image Analysis Society. 4) Microcalcifications (mcc) detection; wavelet transform is used, and to enhance its performance different high-pass filters and high-frequency emphasis filters are evaluated. Symlet wavelets: Sym8 and Sym16 were used with different decomposition level; images results from both processes are compared and only those elements in common are detected as microcalcifications.

Moreover, muscle's remnants in the corners of the regions of interest were removed using fuzzy c-means clustering. The best results in terms of sensitivity (91.27), false-positives per image (80.25), and precision (74.38) are compared with previous work.

Results shows that the breast area can be discriminated from the pectoral-muscle by avoiding to work with brightness areas that produces false positives. Moreover, because the image size is reduced the computer processing time will be decreased. This segmentation stage can be an addition to mammograms analysis broadly, not only to find mcc but abnormalities such as circumscribed masses, speculated masses and architectural distortion. Also is useful to create automatically an unsupervised segmentation in mammograms without stage of training.