Image Processing: Connected Component Analysis and Morphological Feature Extraction

In digital image processing, connected component analysis serves as a fundamental and crucial technique. By extracting features from connected regions in binary images, we can obtain morphological parameters for each distinct object, which hold significant value for object classification, recognition, and measurement. This article explains how to compute key morphological features of connected components: area, perimeter, circularity, rectangularity, and aspect ratio, with implementation insights using common image processing libraries.

Area represents the most basic feature, indicating the total number of pixels within a connected component. Perimeter calculation is more complex, requiring consideration of boundary pixel connectivity—typically implemented using 8-neighborhood or 4-neighborhood boundary tracking algorithms for accurate measurement. In code implementations, the perimeter can be calculated by counting boundary pixels with specific connectivity rules, often utilizing functions like bwperim in MATLAB or findContours in OpenCV followed by arc length calculation. Circularity measures how closely a region resembles a perfect circle, calculated as (4π×area)/perimeter². Values approaching 1 indicate near-perfect circular shapes, with practical implementations involving floating-point precision handling to avoid division errors.

Rectangularity reflects how well a region fits its minimum bounding rectangle, computed as the ratio of region area to minimum bounding rectangle area. This can be implemented using rotating calipers algorithms or functions like minAreaRect in OpenCV. Aspect ratio represents spatial elongation of objects, calculated as the ratio of longer side to shorter side of the minimum bounding rectangle. These combined features effectively describe and differentiate object shapes, providing reliable data support for subsequent image analysis and pattern recognition tasks. In practical applications, feature selection and combination should be determined based on specific task requirements, with considerations for computational efficiency and robustness against noise.