The image shape and texture (appearance) estimation designed for facial recognition is a novel and promising approach for application in breast imaging. and other system parameters. The shape and appearance scores were found to correlate moderately to breast from shape and appearance variables and other SW033291 system outcome parameters generated a model SW033291 with a correlation SW033291 of r2 = 0.8. In conclusion a shape and appearance model exhibited excellent feasibility to extract variables useful for automatic estimation. Further exploring and screening of this approach is usually warranted. values. The method uses a breast tissue-equivalent phantom in the unused portion of the mammogram as a reference to estimate breast composition. To perform quality control monitoring and cross-validation between sites and machines a new altered calibration approach for the SXA method15 was developed. It provides stable thickness measurements and grayscale to density pixel conversion and different machine and sites cross-validation. The cross-calibration is usually achieved by quality control monitoring with specially designed calibration phantom to control thickness and grey-scale conversion stability by the phantom weekly scanning. The new automated approach for volumetric breast density estimation proposed in this paper combines volumetric density steps derived by the SXA method and statistical model building technique based on image parameters extracted from your mammogram. Thus we achieved automatic volumetric breast density estimation from digital mammograms not using the SXA phantom. The image shape and texture estimation (appearance) designed for face recognition seems to be encouraging approach for application in breast imaging to extract the features suitable for statistical model building. Potentially the breast shape and appearance model parameters could give new information useful for breast density estimation breast cancer risk assessment and diagnostics. The purpose of this study is usually to apply a shape and appearance model approach to digital mammograms for automatically quantifying true volumetric fibroglandular tissue volumes from clinical screening full-field digital mammograms without use of phantoms. 2 METHODS Our approach for volumetric breast density estimation is made up in building statistical model using training set of digital mammograms with known steps of percent fibroglandular tissie volume measured by SXA. To derive the model we follow the standard process in supervised machine Rabbit Polyclonal to THOC4. learning: feature generation feature selection regression classification of outputs final model building and validation. The main set of features was generated using shape and appearance model approach. 2.1 Shape and Appearance model The shape and appearance model approach was applied according to the method of Cootes et al16. To create the shape model we used the 137 edge and grid point pairs of x y coordinates inside of the breast area as shown in the SW033291 Fig. 1. The edge line was calculated using global threshold method. Principal Component Analysis (PCA) was applied to extract significant and uncorrelated components. As a preprocessing step an affine transformation was used to remove rotation translation and level. The level factor of this transformation was used as a feature additional to the shape PCA components. In order to build an appearance model first we transformed the breast images into the imply texture image by piecewise linear image transformation. That step aligned all texture information inside the reference imply image. Then a set of significant principal component vectors of appearance model was calculated from image pixels grey-scale values. Finally the shape and texture features of each image were created using principal component vectors. The equations (1-4) describe calculations of the shape (1 2 and appearance (3 4 PCA scores: – x y coordinates; – imply of x y coordinates – PCA shape eigen values vectors and scores – imply and normalized breast values of pixels – PCA appearance eigen values vectors and scores. PCA() function executes PCA transformation. Physique 1 a) The breast with 137 markers and triangulation; b) mean shape breast template with 137 markers and triangulation. SW033291 2.2 Datasets and processing method To build a shape and appearance model we used.