Publication details

Digital Mammogram Enhancement

Monography Chapter

Haindl Michal, Remeš Václav

serial: Mammography Techniques and Review, p. 63-78 , Eds: Fernandes Fabiano Cavalcanti, Brasil Lourdes Mattos, da Veiga Guadagnin Renato

project(s): GA14-10911S, GA ČR

keywords: mammogram enhancement, Markov random field, texture model

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abstract (eng):

Three fully automatic methods for X-ray digital mammogram enhancement based on a fast analytical textural model are presented. These efficient single and double view enhancement methods are based on the underlying two-dimensional adaptive causal autoregressive texture model. The~methods locally predict breast tissue texture from single or double view mammograms and enhance breast tissue abnormalities, such as the sign of a developing cancer, using the estimated model prediction statistics. The~double-view mammogram enhancement is based on the cross-prediction of two mutually registered left and right breasts' mammograms or alternatively a temporal sequence of mammograms. The single-view mammogram enhancement is based on modeling prediction error in case of not the both breasts' mammograms being available.