Comparison of Two Lung Segmentation Algorithms from MRI Temporal Sequences

Renato S. Tavares, Leonardo I. Abe, José M.M. Chirinos, Marcos S.G. Tsuzuki, Toshiyuki Gotoh, Seiichiro Kagei, and Tae Iwasawa


Lung segmentation, Hough transform


The lung segmentation is an intermediary step towards its registry and 3D reconstruction. Usually, segmentation methods are interactive and make use of different strategies to combine the expertise of the human and the computers' accuracy and speed. Lung MRI segmentation is particularly difficult because of the large variation in image quality. Two methods for the lung contour segmentation are presented. In the first method, an individual analysis of each image in the series approach is taken, and the segmentation is performed through thresholding and labeling techniques. In the second method, the breathing is associated to a standard respiratory function, and through 2D image processing, edge detection and Hough transform (HT), respiratory patterns are obtained and, consequently, the position of points in time are estimated. Temporal sequences of MRI are segmented by considering the coherence in time. This way, a cloud of points that approximates the lung silhouette can be determined in every frame, even on frames with obscure edges. The lung region is segmented from the determined cloud of points.

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