Precise Segmentation Rendering for Medical Images Based on Maximum Entropy Processing

Abstract

Precision is definitely required in medical treatments, however, most three-dimensional (3-D) renderings of medical images lack for required precision. This study aimed at the development of a precise 3-D image processing method to discriminate clearly the edges. Since conventional Computed Tomography (CT), Positron Emission Tomography (PET), or Magnetic Resonance Imaging (MRI) medical images are all slice-based stacked 3-D images, one effective way to obtain precision 3-D rendering is to process the sliced data with high precision first then to stack them together carefully to reconstruct a desired 3-D image. A recent two-dimensional (2-D) image processing method known as the entropy maximization procedure proposed to combine both the gradient and the region segmentation approaches to achieve a much better result than either alone seemed to be our best choice to extend it into 3-D processing. Three examples of CT scan data of medical images were used to test the validity of our method. We found our 3-D renderings not only achieved the precision we sought but also has many interesting characteristics that shall be of significant influence to the medical practice.