A fully automated complete segmentation scheme for mammograms

  • Authors:

  • Stylianos Tzikopoulos;Harris Georgiou;Michael Mavroforakis;Nikos Dimitropoulos;Sergios Theodoridis

  • Affiliations:

  • National and Kapodistrian University of Athens, Dept. of Informatics and Telecommunications, Panepistimiopolis, Ilissia, Athens, Greece;National and Kapodistrian University of Athens, Dept. of Informatics and Telecommunications, Panepistimiopolis, Ilissia, Athens, Greece;National and Kapodistrian University of Athens, Dept. of Informatics and Telecommunications, Panepistimiopolis, Ilissia, Athens, Greece;Delta Digital Imaging, Semitelou, Athens;National and Kapodistrian University of Athens, Dept. of Informatics and Telecommunications, Panepistimiopolis, Ilissia, Athens, Greece

  • Venue:
  • DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
  • Year:
  • 2009

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Abstract

This paper presents a fully automated complete segmentation method for mammographic images. Image preprocessing techniques are first applied to mammograms to remove the noise and then a breast boundary extraction algorithm is implemented, in order to distinguish breast tissue from the background. Next, an improved version of an existing pectoral muscle scheme is performed and a new nipple segmentation technique is applied, detecting the nipple when it is in profile. This improves the estimated breast boundary and serves as a key-point for further processing of the image. This composite method has been implemented and applied to miniMIAS, one of the most well-known mammographic databases. This database consists of 322 mediolateral oblique (MLO) view mammograms, obtained via a digitization procedure. The results are evaluated by an expert radiologist and are very promising. Accordingly, it is expected that this procedure can produce improved results, when applied to high-quality dtgital mammograms.