In general, the threshold used in edge detection algorithms plays a critical role in determining the quality of the detected edges. A high threshold will result in only the strongest edges being detected, while a low threshold will detect more edges but may also include more noise and false edges.
In your specific case, it sounds like the threshold you are using is not providing good results. One way to address this issue would be to try using a different thresholding method, such as Otsu's method, which can automatically determine an appropriate threshold based on the image intensity histogram.
In MATLAB, you can use the graythresh function with the 'Otsu' method to apply Otsu's thresholding to an image. Here is an example of how you can use this function to determine an appropriate threshold for edge detection:
% Load the image
I = imread('microcell_image.png');
% Use Otsu's method to determine the threshold
threshold = graythresh(I, 'Otsu');
% Use the threshold to detect edges in the image
edges = edge(I, 'Canny', threshold);
Alternatively, you can try using the adaptivethreshold function to apply adaptive thresholding to the image, which can be more effective at detecting edges in images with varying contrast. Here is an example of how you can use this function to perform adaptive thresholding on an image:
% Load the image
I = imread('microcell_image.png');
% Use adaptive thresholding to detect edges in the image
edges = edge(I, 'Canny', adaptivethreshold(I));
Using either of these approaches should provide better results than the thresholding method you are currently using, and may help to improve the quality of the edge detection in your microcell images.
