Abstract
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.
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Acknowledgements
The authors would like to thank RGCIRC, Delhi, India, for its help and cooperation in providing the required data set of breast MR images. Authors also wish to thank Dr. S. Avinash Rao (Sr. radiologist) for his guidance and help in making ground truth and validating the results.
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Kumari, V., Sheoran, G., Kanumuri, T. et al. Statistical Modelling and Mapping of Intensity Spectrum in Breast MR Images. MAPAN 36, 859–867 (2021). https://doi.org/10.1007/s12647-021-00469-7
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DOI: https://doi.org/10.1007/s12647-021-00469-7