MUTUAL INFORMATION AND GENETIC ALGORITHM BASED REGISTRATION OF MRI BRAIN IMAGES

Registration of intraoperative fluoroscopy images with preoperative two-dimensional and/or three-dimensional MRI images can be used for several purposes in image-guided surgery. Registration is an important problem and a fundamental task in image processing technique. In the medical image processing fields, some techniques are proposed to find a geometrical transformation that relates the points of an image to their corresponding points of another image. In recent years, multimodality image registration techniques are proposed in the medical imaging field. Especially, Mutual information (MI) is currently one of the most effective similarities metric in medical image registration. It is an automatic measure, and suitable for multimodal medical image registration. In radiotherapy planning manual registration techniques performed on MR image and CT images of the brain. Now-a-days, physicians segment the volume of interest (VOIs) from each set of slices manually. However, manual segmentation of the object area may require several hours for analysis. Furthermore, MDCT images and MR images contain more than 100 slices. Therefore, manual segmentation and registration method cannot apply for clinical application in the head CT and MR images. Mutual information (MI) is an automatic measure, and suitable for multimodal medical image registration. The genetic algorithm has been used to predict the deformation due to inclination of object considering mutual information. In this paper, image registration is coupled with mutual information and genetic algorithm. The primary objective of this paper is to increase accuracy of the registration. Experiments show our algorithm is a robust and efficient method which can yield accurate registration results.