Abstract

Marching cubes is the most widely used isosurface algorithm for 3D reconstruction. For the case study, this paper used medical data from an MRI of brain images, especially in the corpus callosum (CC) part, and volume data from the stagbeetle dataset. This case study was selected to highlight the clinical importance of 3D image visualization. This study can help by showing solid anatomy shapes and locations, which can direct the location of a brain injury with a small error of less than 1 mm; therefore, it can support and minimize the risk of brain surgery. The case study is part of the brain called the corpus callosum, usually used as a reference for brain surgery. For the input data, this paper used 2D segmentation using deep learning methods to obtain the CC segments. This paper used 120 patients, 80% for training and 20% for testing from national hospitals. This paper found 11 sagittal slices containing the corpus callosum out of 166 slices for each patient. This work presents an improved MC algorithm that adds twenty new rules to the existing one, strengthening the rules for voxel representation and increasing the original marching cubes algorithm's 15 rules to 35. Thus, large holes are covered in the 3D reconstruction model, making it largely solid. The proposed 3D visualization achieved zero open edges for the datasets from the national hospital. The results showed that applying the improved marching cubes algorithm produced a 3D representation with better and more robust results, as evidenced by the presence of more vertices and triangles and the absence of open edges. Advanced marching cubes are a great way to remove open edges.

Original languageEnglish
Pages (from-to)536-554
Number of pages19
JournalInternational Journal of Intelligent Engineering and Systems
Volume17
Issue number1
DOIs
Publication statusPublished - 2024

Keywords

  • 2D segmentation
  • 3D visualization
  • Brain MRI
  • Corpus callosum
  • Deep learning
  • Marching cubes

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