Generation of 3D Tumor Models from DICOM Images for Virtual Planning of its Recession

• Main Authors: Oscar Rodríguez-Bastidas, Hermes Fabián Vargas-Rosero, M.Sc.
• Format: Article
• Language: English
• Published: Universidad Pedagógica y Tecnológica de Colombia 2020-04-01
• Series: Revista Facultad de Ingeniería
• Subjects: 3d mesh; 3d model; image segmentation; k-means; medical images; usability
• Online Access: https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10173

Medical images are essential for diagnosis, planning of surgery and evolution of pathology. The advances in technology have developed new techniques to obtain digital images with more details, in return this has also led to disadvantages, such as: the analysis of large volumes of information, long time to determine an affected region and difficulty in defining the malignant tissue for its later extirpation, among the most relevant. This article presents an image segmentation strategy and the optimization of a method for generating three-dimensional models. A prototype was implemented in which it was possible to evaluate the segmentation algorithms and 3D reconstruction technique, allowing to visualize the tumor model from different points of view through virtual reality. In this investigation, we evaluate the computational cost and user experience, the parameters selected in terms of computational cost are the time and consumption of RAM, we used 140 MRI images each with dimensions 260x320 pixel, and as a result, we obtained an approximate time of 37.16s and consumption in RAM of 1.3GB. Another experiment carried out is the segmentation and reconstruction of a tumor, this model is formed by a three-dimensional mesh made up of 151 vertices and 318 faces. Finally, we evaluate the application, with a usability test applied to a sample of 20 people with different areas of knowledge. The results show that the graphics presented by the software are pleasant, they also show that the application is intuitive and easy to use. Additionally, it is mentioned that it helps improve the understanding of medical images.