MATLAB Code Implementation for Image Segmentation with Lab Color Space

Implementing image segmentation based on the Lab color space in MATLAB provides a straightforward and effective approach, particularly suitable for processing images with significant color variations. The Lab color space is widely used in color segmentation tasks due to its alignment with human visual perception. The Lab color space decomposes an image into three components: L (lightness), a (green-red axis), and b (blue-yellow axis), making color distinction more intuitive. Leveraging MATLAB's built-in functions, such as `rgb2lab` for color space conversion, we can then apply clustering or thresholding methods for segmentation. The implementation approach typically involves the following steps: - Read an RGB image and convert it to Lab color space using `rgb2lab` function - Extract the a and b channels since they directly represent color information and are ideal for color-based segmentation - Apply K-means clustering or manual thresholding to partition the a and b channels into distinct regions - Generate masks based on clustering or thresholding results to extract target regions This method works well in scenarios where objects and backgrounds have distinct color differences, such as fruit sorting applications and medical image analysis. MATLAB's high-level functions enable concise and readable code implementation, making it accessible for beginners to quickly grasp image segmentation techniques. Key implementation details include: - Using `imread` to load RGB images - Applying `rgb2lab` for accurate color space conversion - Employing `kmeans` function for clustering color components with appropriate cluster numbers - Creating binary masks using logical operations or `imbinarize` for threshold-based approaches - Utilizing `label2rgb` for visualizing segmentation results when needed