process 
veget. 
tidal fl 
ine + 
ottom 
  
lis- 
le 
T 
le 
nth) 
le 
  
process 
path 
  
  
iporal) 
STATISTICAL AND STRUCTURAL APPROACH TO TEXTURE 
By 
R.M.Haralick 
Department of Electrical Engineering, Department of Computer 
Science, The University of Kansas, Lawrence, Kansas 66045, USA 
ABSTRACT 
In this survey we review the image processing literature on the 
various approaches and models investigators have used for texture. 
These include statistical approaches of autocorrelation function, 
optical transforms, digital transforms, textural edgeness, struc- 
tural element, gray tone co-occurrence, run lengths, and auto- 
regressive models. We discuss and generalize some structural 
approaches to texture based on more complex primitives than gray 
tone. We conclude with some structural-statistical generalizations 
which apply the statistical techniques to the structural primitives. 
1.0 INTRODUCTION 
Texture is an important characteristic for the analysis of many 
types of images. It can be seen in all images from multi-spectral 
scanner images obtained from aircraft or satellite platforms 
(which the remote sensing community analyzes) to microscopic 
images of cell cultures or tissue samples (which the bio-medical 
community analyzes). Despite its important and ubiquity in image 
data, a formal approach or precise definition of texture does 
not exist. The texture discrimination techniques are, for the 
most part, ad-hoc. In this paper we survey, unify, and generalize 
some of the extraction techniques and models which investigators 
have been using to measure textural properties. 
The image texture we consider is non-figurative and cellular. We 
think of this kind of texture as an organized area phenomena. When 
it is decomposable, it has two basic dimensions on which it may 
be described. The first dimension is for describing the primitives 
out of which the image texture is composed, and the second dimen- 
sion is for the description of the spatial dependence or inter- 
action between the primitives of an image texture. The first 
dimension is concerned with tonal primitives or local properties, 
and the second dimension is concerned with the spatial organization 
of the tonal primitives. 
Tonal primitives are regions with tonal properties. The tonal 
primitive can be described in terms such as the average tone, or 
maximum and minimum tone of its region. The region is a maximally 
connected set of pixels having a given tonal property. The tonal 
region can be evaluated in terms of its area and shape. The tonal 
primitive includes both its gray tone and tonal region properties. 
An image texture is described by the number and types of its 
primitives and the spatial organization or layout of its primitives. 
The spatial organization may be random, may have a pairwise de-