A comparative survey on three-dimensional reconstruction of medical modalities based on various approaches

Abstract

The area of three-dimensional reconstructions has made advances in the recent years. Image reconstruction is the mathematical process which converts the signals obtained from the scanning machine into an image. Particularly in the medical field, the reconstructed images aid in the surgery and research. This survey provides an overview of three-dimensional reconstruction techniques in medical images using various imaging modalities like MRI, CT, biplanar radiography, and light microscopy along with the related disease. The reconstruction techniques such as Marching Cubes, Delaunay’s Triangulation, Outlier Removal, Edge Enhancement and Binarization, False Positive Pruning, Contours, Support Vector Machines, Poisson Surface Reconstruction, Dictionary Learning, and Parametric Models are briefly described. The advantages and disadvantages of each technique are discussed and some possible future directions are suggested.