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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.

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Segmentation of the lung is particularly difficult because of the large variation in image quality. A modified Hough transform in combination with a mask creation algorithm can robustly determine synchronous respiratory patterns. The synchronicity restriction is relaxed by applying a greedy active contour algorithm. The respiratory patterns define a point cloud near the lung region boundary representing a subjective contour. The gravitation vector field (GVF) active contour algorithm is used to create an initial segmentation exclusively based on the point cloud. A final active contours algorithm is executed to adjust the boundary to the images. The algorithm was tested with healthy subjects and COPD patients, and the result was checked through temporal registration of coronal and sagittal images.

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