Small vessel enhancement in MRA images using local maximum mean processing.
The enhancement of small vessels in MRA imaging is an important problem. In this paper, we propose using local maximum mean (LMM) processing to enhance the detectability of small vessels. At each voxel in the original three-dimensional (3-D) data set, the LMM over the line segments in the cube centered at the voxel is taken and used to form the 3-D LMM data set. The maximum intensity projection (MIP) is then applied to the LMM data to produce the two-dimensional (2-D) LMM-MIP image. Through LMM processing, the variance of background tissue is reduced, thus increasing the detectability of small vessels. Moreover, the single bright voxels are suppressed and the disconnected small vessels can be connected. However, the LMM processing widens the larger, brighter vessels. To keep the advantages provided by both the LMM-MIP and MIP images, it is proposed that weight functions be used to combine them. The performance of the LMM-MIP algorithm is analyzed and compared with the performance of the MIP algorithm under three measures: The vessel voxel projection probability, the vessel receiver operating characteristic (ROC) curve and the vessel-tissue contrast-to-noise ratio (CNR). Closed forms of the three measures are obtained. It is shown that the LMM-MIP algorithm improves the detectability of small vessels under all three measures. The longer the projection path and the larger the CNR of the original data, then the greater the improvement. Confirming the theoretical analysis, results of an experiment utilizing practical MRA data demonstrate the improved visual quality of small vessels.
Sun
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Department of Electrical Engineering, The City College of New York, New York, NY 10031, USA. sun@ccny.cuny.edu
Parker
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D