Table 5: Summary of representative studies using statistical model building methods for mammographic tissue segmentation. Note that () this list consists of studies mainly related to Tabár tissue modelling and () in case of multiple results, only the best reported results are listed.

Study Year Number of mammographic building blocks Modalities Number of views Number of images Segmentation evaluation Risk/density estimation accuracy

Texture statistical variation
Muhimmah et al. [71] 2007 Linear, nodular, homogeneous, and radiolucent Digitised SFM MLO 320 (MIAS) Visually assessed N/A
He et al. [72] 2008 Linear, nodular, homogeneous, and radiolucent Digitised SFM MLO 320 (MIAS) Visually assessed N/A
He et al. [73] 2009 Linear, nodular, homogeneous, and radiolucent Digitised SFM MLO 320 (MIAS) Visually assessed (65% good/very good) Tabár (38% good/very good) BI-RADS 53% (Tabár), 70% (BI-RADS)
He et al. [74] 2011 Linear, nodular, homogeneous, and radiolucent Digitised SFM MLO 320 (MIAS) Visually assessed 53% (Tabár)
He et al. [75] 2012 Linear, nodular, homogeneous, and radiolucent Digitised SFM MLO 320 (MIAS) Visually assessed 85% (Tabár), 78% (BI-RADS)
He et al. [76] 2014 Nodular, homogeneous, and radiolucent FFDM MLO and CC 360 Visually assessed 79% (Tabár), 80% (BI-RADS)

Texture descriptors
He et al. [77] 2010 Linear, nodular, homogeneous, and radiolucent Digitised SFM MLO 320 (MIAS) Visually assessed 78% (Tabár), 75% (BI-RADS)
He and Zwiggelaar [78] 2013 Nodular, homogeneous, and radiolucent FFDM MLO and CC 360 Visually assessed N/A