Statistical Modelling and Mapping of Intensity Spectrum in Breast MR Images

Tissue segregation plays a crucial role in the measurement of breast density in breast magnetic resonance (MR) images. This paper proposes a mathematical analysis of the new distribution mixture model for the intensity spectrum of breast MR images using Gamma and Gaussian distribution for fibro-glandular and adipose tissues, respectively. The thorough regression analysis and mapping presented in this paper clearly indicate that the distribution of Gamma is best suited to the spectrum of fibro-glandular tissue intensities relative to the standard Gaussian distribution. Moreover, Gamma distribution can represent both symmetric and non-symmetric (skewed) intensity distributions in a more efficient way, leading to a more accurate segmentation of fibro-glandular and adipose tissues. The efficiency of the segmentation is quantified by measuring the standard performance appraisal steps : Dice similarity coefficient, Jaccard index and dissimilarity index. The whole mathematical analysis is performed on a data set of 200 patients with 160 axial slices per subject with various breast sizes and densities. The Gamma Gaussian mixture model (GaGMM’s) assessment metrics indicate an improvement of 39.4 %, 46.8 % and 54.9 %, respectively, in relation to the Gaussian mixture model.