Breast density has been shown to be an independent risk factor for breast cancer. In order to segment breast parenchyma, which has been proposed as a biomarker of breast cancer risk, we present an integrated algorithm for simultaneous T1 map estimation and segmentation, using a series of magnetic resonance (MR) breast images. The advantage of using this algorithm is that the step of T1 map estimation (E-Step) and the step of T1 map based tissue segmentation (S-Step) can benefit each other. Since the estimated T1 map can be noisy due to the complexity of T1 estimation method, the tentative tissue segmentation results from S-Step can help perform the edge-preserving smoothing on the estimated T1 map in E-Step, thus removing noises and also preserving tissue boundaries. On the other hand, the improved estimation of T1 map from E-Step can help segment breast tissues in a more accurate and less noisy way. Therefore, by repeating these steps, we can simultaneously obtain better results for both T1 map estimation and segmentation. Experimental results show the effectiveness of the proposed algorithm in breast tissue segmentation and parenchyma volume measurement.