Similarity measure plays a significant task in intensity-based image registration. Nowadays, mutual information (MI) has been used as an efficient similarity measure for multimodal image registration. MI reflects the quantitative aspects of the information as it considers the probabilities of the voxels. However, different voxels have distinct efficiency towards the gratification of the elementary target, which may be self-reliant of their probability of occurrence. Therefore, both intensity distributions and effectiveness are essential to characterise a voxel. In this study, a novel similarity measure has been proposed by integrating the effectiveness of each voxel along with the intensity distributions for computing the enhanced MI using joint histogram of the two images. Penalised spline interpolation is incorporated to the joint histogram of the similarity measure, where each grid point is penalised with a weighted factor to avoid the local extrema and to achieve better registration accuracy as compared with existing methods with efficient computational runtime. To demonstrate the proposed method, the authors have used a challenging medical image dataset consisting of pre- and post-operative brain magnetic resonance imaging. The registration accuracy for the dataset improves the clinical diagnosis, and detection of growth of tumour in post-operative image.
['Smita Pradhan', 'Dipti Patra']
Enhanced mutual information based medical image registration