The selection and evaluation of grey-level thresholds applied to digital images

Abstract

Many applications of image processing require the initial segmentation of the image by means of grey-level thresholding. In this thesis, the problems of automatic threshold selection and evaluation are addressed in order to find a universally applicable thresholding method. Three previously proposed threshold selection techniques are investigated, and two new methods are introduced. The results of applying these methods to several different images are evaluated using two threshold evaluation techniques, one subjective and one quantitative. It is found that no threshold selection technique is universally acceptable, as different methods work best with different images and applications