Non-rigid image registration with uniform gradient spherical patterns.
In this paper, we propose a new feature based non-rigid image registration method for dealing with two important issues. First, in order to establish reliable anatomical correspondence between template and subject images, efficient and distinctive region descriptor is needed as intensity information alone maybe insufficient. Second, since interference factors such as monotonic gray-level bias fields are commonly existed during the imaging process, the registration algorithm should be robust against such factors. There are two main contributions presented in this paper. (1) A new region descriptor, named uniform gradient spherical pattern (UGSP), is proposed to extract the geometric features from input images. UGSP encodes second order voxel interaction information. (2) The UGSP feature is rotation and monotonic gray-level bias field invariant. The proposed method is integrated with the Markov random field (MRF) labeling framework to formulate the registration process. The alpha-expansion algorithm is used to optimize the corresponding MRF energy function. The proposed method is evaluated on both the simulated and real 3D databases obtained from BrainWeb and IBSR respectively and compared with other state-of-the-art registration methods. Experimental results show that the proposed method gives the highest registration accuracy among all the compared methods on both databases.
Liao
Shu
S
Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong. liaoshu@cse.ust.hk
Chung
Albert C S
AC
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique