Medical Image Segmentation and Reconstruction Based on Bayesian Level Set Method and Marching Cubes Algorithm

Abstract

We propose an approach, integrating Bayesian level set method with modified marching cubes algorithm for brain tissue and tumor segmentation and surface reconstruction. First, we extend the level set method based on the Bayesian risk to three-dimensional segmentation. Then, the three-dimensional Bayesian level set method is used to segment solid three-dimensional targets (e.g., tissue, whole brain, or tumor) from serial slice of medical images. Finally, the modified marching cubes algorithm is used to continuously reconstruct the surface of targets. Since each step can definitely obtain an appropriate treatment by statistical tests, the tissue and tumor segmentation and surface reconstruction are expected to be satisfied.

Large scale medical image online three-dimensional reconstruction based on WebGL using four tier client server architecture

“…As a branch of scientific computational visualization, 3D reconstruction visualization has essential applications and broad prospects in the fields of medical diagnosis and adjuvant therapy, such as radiotherapy, medical research and education, and surgical planning and simulation. 3 3D visualization technology can display the 3D graphic data of medical images to diagnostic personnel to observe the image data of patients from multiple views and scales, which can assist doctors in carrying out qualitative and quantitative analysis of pathological tissues, thus improving the accuracy and efficiency of diagnosis and treatment. 4 The research results by Wu et al 5 in 2020 show that 3D reconstruction can provide sufficient information on the anatomical relationship among tumor, renal vessels, and collection system and clearly identify the number and location of tumor feeding arteries.…”