Registration of Brain MRI/PET Images Based on Adaptive Combination of Intensity and Gradient Field Mutual Information.
Traditional mutual information (MI) function aligns two multimodality images with intensity information, lacking spatial information, so that it usually presents many local maxima that can lead to inaccurate registration. Our paper proposes an algorithm of adaptive combination of intensity and gradient field mutual information (ACMI). Gradient code maps (GCM) are constructed by coding gradient field information of corresponding original images. The gradient field MI, calculated from GCMs, can provide complementary properties to intensity MI. ACMI combines intensity MI and gradient field MI with a nonlinear weight function, which can automatically adjust the proportion between two types MI in combination to improve registration. Experimental results demonstrate that ACMI outperforms the traditional MI and it is much less sensitive to reduced resolution or overlap of images.
Liu
Jiangang
J
Medical Image Processing Group, Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Science, P.O. Box 2728, Beijing 100080, China.
Tian
Jie
J
Med Image Anal. 1999 Dec;3(4):373-86
10709702
IEEE Trans Med Imaging. 2000 Aug;19(8):809-14
11055805
Med Image Anal. 2001 Jun;5(2):111-26
11516706
Med Image Anal. 2001 Jun;5(2):143-56
11516708
IEEE Trans Med Imaging. 2002 May;21(5):450-61
12071616
Comput Med Imaging Graph. 2002 Jul-Aug;26(4):277-92
12074923
Med Image Anal. 1996 Jun;1(2):151-61
9873926
IEEE Trans Med Imaging. 2003 Jul;22(7):854-64
12906239
IEEE Trans Med Imaging. 2003 Aug;22(8):986-1004
12906253
Ann Nucl Med. 2004 Dec;18(8):659-67
15682846
IEEE Trans Med Imaging. 1997 Apr;16(2):187-98
9101328
J Comput Assist Tomogr. 1997 Jul-Aug;21(4):554-66
9216759
IEEE Trans Med Imaging. 2003 Feb;22(2):217-27
12715998