Implementation of Human Brain Segmentation in Medical Images Using C Language on MATLAB Platform

The implementation of human brain segmentation in medical images is developed on the MATLAB platform using C language programming. To achieve this objective, we leverage MATLAB's Image Processing Toolbox and C language programming techniques to develop a specialized segmentation algorithm. The algorithm extracts characteristic features of the human brain from medical images and performs segmentation based on these features. Specifically, we implement custom C language modules to handle image data processing operations, while MATLAB serves as the primary development platform to coordinate and manage the entire segmentation workflow. The C language components are integrated through MATLAB's MEX interface, allowing efficient low-level image processing operations such as pixel-wise calculations, morphological operations, and region-based analysis. Key algorithmic approaches include threshold-based segmentation combined with region growing techniques for brain tissue identification. The implementation utilizes MATLAB's built-in functions for image preprocessing (filtering, enhancement) and visualization, while C modules handle computationally intensive tasks like 3D volume processing and memory-efficient data manipulation. By combining MATLAB's rapid prototyping capabilities with C language's execution efficiency, we achieve high-performance and accurate human brain segmentation. This integrated approach provides significant support for research and applications in medical image processing, particularly in neuroimaging studies where precise brain structure identification is crucial. The system architecture allows for easy customization of segmentation parameters and supports various medical image formats including DICOM and NIfTI.