MATLAB Implementation for Medical Image Processing

Medical image processing employs mathematical morphology techniques primarily involving erosion, dilation, opening, and closing operations. These methods are crucial for analyzing medical imaging modalities such as CT scans and MRI, enabling feature extraction and enhancement of critical anatomical structures. Through medical image processing algorithms, healthcare professionals and researchers can achieve improved interpretation and quantitative analysis of medical image data, leading to more accurate diagnostics and treatment planning. Key MATLAB implementations typically utilize the Image Processing Toolbox functions: - `imerode()` for erosion operations to shrink image regions - `imdilate()` for dilation processes to expand object boundaries - `imopen()` (erosion followed by dilation) for noise removal - `imclose()` (dilation followed by erosion) for gap filling These morphological operations are commonly applied to binary or grayscale medical images using structuring elements created with `strel()` function, which defines the shape and size for neighborhood operations. Implementation typically involves preprocessing steps like image enhancement, segmentation, followed by morphological processing to extract meaningful clinical information.