Statistical Modelling and Mapping of Intensity Spectrum in Breast MR Images

• Published in: MĀPAN : journal of Metrology Society of India Vol. 36; no. 4; pp. 859 - 867
• Main Authors: Kumari, Vineeta, Sheoran, Gyanendra, Kanumuri, Tirupathiraju, Barak, Neelam, Koul, Prajval
• Format: Journal Article
• Language: English
• Published: New Delhi Springer India 01.12.2021; Springer Nature B.V
• Subjects: Adipose tissue; Image segmentation; Magnetic resonance imaging; Mapping; Mathematical analysis; Mathematical and Computational Physics; Mathematical Methods in Physics; Measurement Science and Instrumentation; Normal distribution; Numerical and Computational Physics; Original Paper; Performance appraisal; Physics; Physics and Astronomy; Probabilistic models; Probability distribution functions; Regression analysis; Simulation; Skewed distributions; Statistical analysis; Statistical models; Theoretical; Gaussian mixture model (GMM); Breast MRI; Tissue segmentation; Regression analysis; Gamma Gaussian mixture model (GaGMM)
• ISSN: 0970-3950; 0974-9853
• DOI: 10.1007/s12647-021-00469-7

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.