I almost never use Otsu. It's usually no good. It's good mostly in cases with high contrast images with widely separated bimodal histograms, like where there is a uniform object on a uniform background and they have very different intensities. In real world situations, Otsu is often/usually not the best choice.
Actually using a fixed threshold like you're doing is a very good but under-used and under-appreciated method. People always want to use automatic thresholding but simple-minded autothresholding methods like Otsu have the drawback in that they always have to find a threshold somewhere in the histogram. Let's say you're looking at some image and the disease or stuff you want to find can be anywhere from 0% area fraction all the way up to 100% area fraction. Let's say you have a hump centered at 100 for good/normal/non-diseased pixels, and as the pixel goes "bad" the pixels get darker, which means their histogram shifts darker. Let's say one that was 100% bad would give you a hump at 50 gray levels. But what if you had a 100% bad image and Otsu would try to split that histogram and might give you something around 48 or 55 or whatever. It would give you the wrong area fraction because it was forced to find a threshold in a histogram hump that was essentially all the same - all bad. It would be much better to just say that anything below, say, 75 is bad and above 75 is good. That way you can get accurate area fractions all the way from 0% to 100%. If you have good control over your lighting and exposure so that similar images produce similar gray levels, then using a fixed threshold is a fine idea.
But in general, you need to decide upon a thresholding scheme based on the complete range of images that you expect to encounter. You haven't shown us even a single image, much less the entire span of images you expect the algorithm to be able to handle. Usually people asking for image processing advice attach an image to we don't have to work "blind."
