21 May 1999 Hierarchical Markov random field modeling for mammographic structure segmentation using multiple spatial and intensity image resolutions
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Abstract
A hierarchical Markov random field (MRF) model for mammographic structure segmentation using multiple spatial and intensity image resolutions is proposed. The general image model is formed by a sequence of representations at different spatial and intensity scales. Through the hierarchical structure of the MRF model, components at different local spatial resolutions are used to condition the corresponding intensity resolution and the spatial distribution of the intensity components. As a first step, only breast skin edge and non fat breast parenchyma (Cooper's ligaments, blood vessels and fibroglandular tissue) have been included into the model and implemented. Three basic priors for the local spatial intensity distribution (texture) are defined. An iterated conditional mode (ICM) optimization procedure is implemented, the lower resolution representations are used sequentially to form the initial image configurations for the ICM procedure. The proposed approach was tested using 100 digitized mammograms (at a resolution of 100 microns and 12 bits per pixel). The mammograms are from three different views and different breast parenchyma densities. Results for breast skin edge and breast parenchyma were obtained and evaluated visually. For all cases, the location of the three possible structures (skin, parenchyma and background) was identified correctly.
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Rene Vargas-Voracek, Rene Vargas-Voracek, Carey E. Floyd, Carey E. Floyd, } "Hierarchical Markov random field modeling for mammographic structure segmentation using multiple spatial and intensity image resolutions", Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348570; https://doi.org/10.1117/12.348570
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