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In this work, segmentation is an intermediate step in the registration and 3D reconstruction of the lung, where the diaphragmatic surface is automatically and robustly isolated. Usually, segmentation methods are interactive and use different strategies to combine the expertise of humans and computers. Segmentation of lung MR images is particularly difficult because of the large variation in image quality. The breathing is associated to a standard respiratory function, and through 2D image processing, edge detection and Hough transform, respiratory patterns are obtained and, consequently, the position of points in time are estimated. Temporal sequences of MR images are segmented by considering the coherence in time. This way, the lung silhouette can be determined in every frame, even on frames with obscure edges. The lung region is segmented in two steps: a mask containing the lung region is created, and the Hough transform is applied exclusively to mask pixels. The shape of the mask can have a large variation, and the modified Hough transform can handle such shape variation. The result was checked through temporal registration of coronal and sagittal images.