Circle Detection in Grayscale Images using MATLAB

Using MATLAB, we can implement image processing techniques to detect circular regions in grayscale images. This approach enables the extraction of desired circular areas, facilitating better understanding and analysis of image data. The implementation typically involves several key algorithms and techniques including threshold segmentation, edge detection, and Hough transform.

For threshold segmentation, MATLAB's imbinarize() or graythresh() functions can be used to convert grayscale images to binary format. Edge detection algorithms like edge() with Canny or Sobel operators help identify circular boundaries. The Circular Hough Transform implemented through imfindcircles() function is particularly effective for detecting circles with specified radius ranges.

Parameter optimization involves adjusting sensitivity thresholds, minimum/maximum radius values, and edge detection parameters to improve detection accuracy. MATLAB's vision.BlobAnalysis can further analyze detected circular regions for properties like area, centroid, and perimeter. Additional image processing methods such as morphological operations using imopen() or imclose() can refine the results, while region properties analysis with regionprops() enables quantitative measurement of detected circles for various application requirements.