Design and evaluation of an expert system based on histogram shape for image thresholding

In this study, a method of thresholding is proposed that based on the histogram shape of each image, proposes the more appropriate technique from the famous and common thresholding techniques. Thresholding, which is a commonly used operation for image processing, is the selection of one of the image pixels that determines the border for background and foreground of the image. After determining a suitable threshold, the image can be converted to a binary image and from this binary image, which has a very small size, informations can be extracted for utilization in various scientific subjects. From histogram tests of images, examination of criteria extracted from the histogram shape of each image, and empirical knowledge, we designed an expert system that proposes the suitable thresholding technique for an image based on the histogram of that image. We used modeling and matching for designing this system in such way that a model was produced from the histogram of the most suitable result for each thresholding technique and was stored on a knowledge base. Afterwards, a system is coded to receive the input image and after matching this image with the above models, the technique with the most model proximity to the histogram of the input image is proposed as the more appropriate technique from the methods of thresholding. Nowadays thresholding, which is the preprocessing for each image process, is widely used. So far, many techniques and methods of thresholding have been proposed. Although all the techniques are useful, the results vary for each image. Sometimes a certain technique is more appropriate for an image than the rest of the techniques. Based on experimentation and qualitative reasoning which will be mentioned later, we concluded what techniques are more appropriate for different shapes of histograms. The application of the proposed method is to facilitate selection of a suitable thresholding technique for an image. Furthermore, the proposed method was tested - n many images and the results showed that by using this method, choosing a suitable thresholding method can be greatly automatized.