3D Brain Visualization Through CT Image Reading and Reconstruction

In medical image processing, CT scanning is a common imaging modality. This method utilizes X-ray scanning of the human body to acquire multiple two-dimensional images from different angles. These 2D images can be reconstructed into 3D volumetric data, enabling physicians to better understand patient conditions. In this process, MATLAB serves as an efficient tool for processing these images, enhancing their clarity and analytical capabilities. Key implementation aspects include using MATLAB's Image Processing Toolbox functions such as `dicomread()` for reading DICOM format CT images, `imshow3D()` for slice-by-slice visualization, and volume rendering techniques for 3D reconstruction. The reconstruction algorithm typically involves image preprocessing (noise reduction, contrast enhancement), slice alignment, and volumetric data interpolation using methods like nearest-neighbor or trilinear interpolation. By implementing CT image reading and 3D brain visualization, we can effectively learn MATLAB-based CT image three-dimensional reconstruction workflows, ultimately providing better support for medical diagnosis and treatment. The process demonstrates practical applications of computational methods in healthcare imaging systems.