A human body consists of a complex 3D structure. Conversion of 3D structures into 2D leads to a loss of information and may result in incorrect disease diagnosis. This issue has grasped the attention of researchers involved in 3D modeling. MRI scans c...
A human body consists of a complex 3D structure. Conversion of 3D structures into 2D leads to a loss of information and may result in incorrect disease diagnosis. This issue has grasped the attention of researchers involved in 3D modeling. MRI scans consist of a large number of 2D slices, which makes 3D reconstruction a complex and time-consuming task. We propose an efficient algorithm that uses limited MRI slices to reconstruct a 3D image on the basis of matching criteria, which aids in the selection of most appropriate slices, which therefore significantly reduces computational complexity and increases accuracy. The methodology involves the acquisition of a brain MRI, pre-processing, OTSU’s segmentation for the identification of suspicious areas, and rule-based classification to extract a tumor area. For appropriate slice selection, Rapid Mode image matching is utilized, 3D modeling is performed using a cubic reconstruction scheme, and finally the tumor volume is calculated. Performance of proposed work is tested on the XNAT datasets of 21 patients. We achieved 96.6% accuracy and concluded that it can be efficiently used in all clinical applications.