LITERATURE SURVEY FOR 3D RECONSTRUCTION OF BRAIN MRI IMAGES

Abstract

Since Doctors had only the 2D Image Data to visuali ze the tumors in the MRI images, which never gave tfeel of how the tumor would exactly look like . The doctors were de prived from the exact visualization of the tumor th e amount of the tumor to be removed by operation was not known, which caused a lot of deformation in the faces and structure of th e patients face or skull. The diversity and complexity of tumor cells makes it ve ry challenging to visualize tumor present in magnet ic resonance image (MRI) data. Hence to visualize the tumor properly 2D MRI image has to be converted to 3D image. With the developme nt of computer image processing technology, three-dimensional (3D) visua lization has become an important method of the medi cal diagnose, it offers abundant and accurate information for medical exper ts. Three-dimensional (3-D) reconstruction of medic al images is widely applied to tumor localization; surgical planning and brain electromagnetic field computation etc. The brain MR images have unique characteristics, i.e., very complicated changes of the gray-scales and highly irregular boundaries. Tr aditional 3-D reconstruction algorithms are challenged in solving this problem. Many reconstruction algorithms, such as marching cu bes and dividing cubes, need to establish the topological relationship between t he slices of images. The results of these tradition al approaches vary depending on the number of input sections, their positions, the shape of the original body and the applied interpol ation technique. These make the task tedious and time-consuming. Moreover, satisfie d reconstruction result may not even be obtained wh en the highly irregular objects such as the encephalic tissues are consider ed. Due to complexity and irregularity of each ence phalic tissue boundary, threedimensional (3D) reconstruction for MRI image is ne cessary. A Literature survey is done to study diffe rent methods of 3D reconstruction of brain images from MRI images.