Uploaded by Meroo Bakkar


The GLCM is a tabulation of how often different combinations of
brightness values (grey levels) occur in an image
Through GLCM features we can extract
the textural features of a medical image. GLCM
method always focuses on the pixel intensity level
of the neighboring pixel [6]. GLCM always
accounts for the specific position of pixel relative
to other pixel. This is a simple tabulation that
shows how often the different combinations of
pixel brightness values occur in medical image. By
taking d values as d=l, 2, 3, 4 for the direction of
data given for 0°, 45°, 90°, 180° matrices of GLCM
was constructed [9].P(i,j) represents the probability
that 2 pixels with the specified separation have
gray levels i and j.
GLCM defines the
probability of the occurrence of the combination of one grey
tone at a specified distance and in a direction
.The RGB
image first get converted into gray scale image and given as
input to the GLCM, in which the number of rows and
columns is equal to the number of gray levels
computes the frequency of certain number of gray levels
reappearing at different position in an image[9]. GLCM
feature extraction maps the gray level co-occurrence
probabilities in different angular position based on spatial
relationship between different combination of pixels. The
following features are extracted from matrix based on
GLCM: autocorrelation, Contrast, Energy, Entropy,
A gray level co-occurrence matrix (GLCM) or cooccurrence distribution (less often co-occurrence matrix or
co-occurrence distribution) is a matrix or distribution that is
defined over an image to be the distribution of co-occurring
values at a given offset. A GLCM is a matrix where the
number of rows and columns is equal to the number of gray
levels, G, in the image The use of statistical features is
therefore one of the early methods proposed in the image
processing literature. Haralick [14] suggested the use of cooccurrence matrix or gray level co-occurrence matrix. It
considers the relationship between two neighboring pixels,
the first pixel is known as a reference and the second is
known as a neighbor pixel