COMPUTER VISION, GRAPHICS, AND IMAGE PROCESSING 3t),125-147 (1985) Threshold Selection Based on a Simple image Statistic J. KI~ERAND J. ILLINGWORTH SERC Rutherford Appleion Laboratory, Chilton, Didcot, Oxon, OX11 OQX, United Kingdom AND J. F~GLEIN Institute for Coordination of Computer Techniques, Budapest, Hungary ReceivedJune 19,1984; acceptedDecember 4,1984 The problem of automatic threshold selection is considered. After a brief review of available techniques, a novel method is proposed. It is based on image statistics which can be computed without histogramming the grey level values of the image. A detailed analysis of the properties of the algorithm is then carried out. The effectiveness of the method is shown on a number of practical examples. 0 1985 Academic Press. Inc. 1. INTRODUCTION A primary problem of image processing is to devise algorithms which will successfully divide complex images into areas which meaningfully correspond to objects in the real world. This image segmentation problem can be extremely difficult for general images which contain a large range of luminance or grey level values. However, for many important applications in medicine or industrial inspection, the main features of an image can be represented by as few as two grey levels. A typical example is the inspection of an object placed on a dark background with which it contrasts strongly. In such a situation the histogram of luminance valueswill possess a strong bimodality with one peak corresponding to pixels from the object regions and the other corresponding to pixels of the image background. This observation permits classification or segmentation of the image by considering the relation of the luminance values 1(x, y) with a luminance value T which is between the luminance values of the object and background. The simple decision criterion for the class of each pixel is: if I( x, y) 2 T then pixel is object or class 1 elsepixel is background or class2. T is called a threshold and this paper is concernedwith automatically choosing this number for images which satisfy a two-class assumption. The importance of the thresholding segmentationmethod is based on its simplic- ity and its wide applicability. It is useful becauseit is a data reduction step and becauseit produces a binary representation of an image. Binary images are readily manipulated to produce higher level descriptions of the scenesand objects, i.e., borders, relational graphs, etc. The problems which occur in blindly applying thresholding are due to the nature of real images and the fact that the assumptionswhich underlie the method, i.e., the image is representable using only two grey levels, are not satisfied. A common 125 0734-189X/85 $3.00 Copyright 0 1985 by Academic Press, Inc. All rights of reproduction in any form resaved.