In the previous blog, we discussed otsu’s method for automatic image thresholding. Then we also discussed the limitations of the otsu’s method. In this blog, we will discuss how to handle these limitations so as to produce satisfactory thresholding results. So, let’s get started.

Case-1: When the noise is present in the image

If the noise is present in the image, then this tends to change the modality of the histogram. The sharp valleys between the peaks of the bimodal histogram start degrading. In that case, the otsu’s method or any other global thresholding method will fail. So, in order to find the global threshold, one should first remove the noise using any smoothing filters like Gaussian, etc. and then apply any automatic thresholding method like otsu, etc.

Case-2: When the object area is small compared to the background area

In this case, the image histogram will be dominated by a large background area. This will increase the probability of any pixel belonging to the background. So, the histogram will no longer exhibit bimodality and thus otsu will result in segmentation error. To prevent this, one should only consider pixels that lie on or near the edges between the objects and the background. Doing so will result in an image histogram with peaks of approximately the same size. Then we can apply any automatic thresholding method like otsu, etc. Below are the steps to implement the above procedure.

• Calculate the edge image using any high pass filter like Sobel, Laplacian, etc.
• Select any threshold value (T).
• Threshold the above edge image to produce a binary mask.
• Apply the mask image on the input image using any bitwise operations or any other method.
• This results in only those pixels where the mask image was white.
• Compute the histogram of only those pixels
• Finally, apply any automatic global thresholding method like otsu, etc.

Case-3: When the image is taken under non-uniform illumination conditions

In this case, the histogram no longer remains bimodal and thus we will not be able to segment the image satisfactorily. One of the simplest approaches is to subdivide the image into non-overlapping images/rectangles. The size of these rectangles is chosen such that the illumination is nearly constant in each of these rectangles. Then we will apply any global thresholding technique like otsu for each of these rectangles.

The above procedure only works when the size of the object and the background are comparable in the rectangle. This is quite intuitive as only then we will have a bimodal histogram. Taking care of the background and the object sizes in each rectangle is a tedious task.

So, in the next blog, we will discuss adaptive thresholding that works pretty well for the above conditions. That’s all for this blog. Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.