Segmentation of Breast MR Images using a Generalised 2D Mathematical Model with Inflation and Deflation Forces of Active Contours

Andrik Rampun, Bryan Scotney, PJ Morrow, Hui Wang, John Winder

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5 Citations (Scopus)
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In medical computer aided diagnosis systems, image segmentation is one of the major pre-processing steps used to ensure only the region of interest, such as the breast region, will be processed in subsequent steps. Nevertheless, breast segmentation is a difficult task due to low contrast and inhomogeneity, especially when estimating the chest wall in magnetic resonance (MR) images. In fact, the chest wall is comprises of fat, skin, muscles, and the thoracic skeleton, which can misguide automatic methods when attempting to estimate its location. The objective of the study is to develop a fully automated method for breast and pectoral muscle boundary estimation in MR images. Firstly, we develop a 2D breast mathematical model based on 30 MRI slices (from a patient) and identify important landmarks to obtain a model for the general shape of the breast in an axial plane. Subsequently, we use Otsu’s thresholding approach and Canny edge detection to estimate the breast boundary. The active contour method is then employed using both inflation and deflation forces to estimate the pectoral muscle boundary by taking account of information obtained from the proposed 2D model. Finally, the estimated boundary is smoothed using a median filter to remove outliers. Our two datasets contain 60 patients in total and the proposed method is evaluated based on 59 patients (one patient is used to develop the 2D breast model). On the first dataset the proposed method achieved Jaccard= 81.1% ± 6.1% and dice coefficient= 89.4% ± 4.1% and on the second dataset Jaccard= 84.9% ± 5.8% and dice coefficient= 92.3% ± 3.6%. These results are qualitatively comparable with the existing methods in the literature.
Original languageEnglish
JournalArtificial Intelligence in Medicine
Early online date9 Nov 2018
Publication statusE-pub ahead of print - 9 Nov 2018

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