Image Segmentation Using Region Growing Method

Region growing is a classical image segmentation technique that starts from predefined seed points and gradually merges adjacent pixels into connected regions based on pixel similarity. This method is particularly suitable for processing images with clear boundaries and uniform texture characteristics. In code implementation, this typically involves creating a queue data structure to manage pixels awaiting processing and using neighbor traversal algorithms (like 4-connected or 8-connected) to examine surrounding pixels.

First, the input color image is converted to grayscale. This conversion simplifies the processing pipeline, reduces computational complexity while retaining sufficient information for segmentation. Color images typically contain three RGB channels, whereas grayscale conversion represents each pixel with a single intensity value, facilitating subsequent processing. In programming terms, this can be achieved using functions like rgb2gray() in MATLAB or cv2.cvtColor() in OpenCV with appropriate color conversion codes.

The core of region growing lies in seed point selection and growth rule formulation. Seed points serve as starting positions for segmentation, typically chosen as representative pixels either manually selected or automatically detected through algorithms. During the growth process, the algorithm compares grayscale value differences between the current region and adjacent pixels. If the difference is smaller than a preset threshold, the pixel is incorporated into the current region; otherwise, expansion stops. This similarity judgment ensures internal consistency within segmented regions. In practice, developers often implement threshold comparison using conditional statements and maintain dynamic region lists during pixel aggregation.

The growth process repeats iteratively until no new pixels meet the merging criteria. Ultimately, the image is partitioned into several connected regions where pixels within each region share similar grayscale characteristics, while significant differences exist between regions. Programmatically, this requires implementing while-loop or queue-based processing that continues until all candidate pixels are evaluated.

The advantage of region growing lies in its straightforward implementation, intuitive operation, and good boundary preservation for target objects. However, its effectiveness highly depends on seed point positioning and threshold settings, potentially performing poorly with noisy images or those with uneven grayscale distributions. In practical applications, segmentation quality can be improved by combining preprocessing techniques (like smoothing filters) or post-processing methods (such as region merging). Code implementation often incorporates Gaussian blur for noise reduction and morphological operations for boundary refinement.