1 research outputs found
Color graph representation for structural analysis of tissue images
Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2010.Thesis (Master's) -- Bilkent University, 2010.Includes bibliographical references leaves 71-82.Computer aided image analysis tools are becoming increasingly important in
automated cancer diagnosis and grading. They have the potential of assisting
pathologists in histopathological examination of tissues, which may lead to a
considerable amount of subjectivity. These analysis tools help reduce the subjectivity,
providing quantitative information about tissues. In literature, it has
been proposed to implement such computational tools using different methods
that represent a tissue with different set of image features. One of the most commonly
used methods is the structural method that represents a tissue quantifying
the spatial relationship of its components. Although previous structural methods
lead to promising results for different tissue types, they only use the spatial relations
of nuclear tissue components without considering the existence of different
components in a tissue. However, additional information that could be obtained
from other components of the tissue has an importance in better representing the
tissue, and thus, in making more reliable decisions.
This thesis introduces a novel structural method to quantify histopathological
images for automated cancer diagnosis and grading. Unlike the previous structural
methods, it proposes to represent a tissue considering the spatial distribution
of different tissue components. To this end, it constructs a graph on multiple tissue
components and colors its edges depending on the component types of their
end points. Subsequently, a new set of structural features is extracted from these
“color graphs” and used in the classification of tissues. Experiments conducted
on 3236 photomicrographs of colon tissues that are taken from 258 different patients
demonstrate that the color graph approach leads to 94.89 percent training
accuracy and 88.63 percent test accuracy. Our experiments also show that the
introduction of color edges to represent the spatial relationship of different tissue components and the use of graph features defined on these color edges significantly
improve the classification accuracy of the previous structural methods.Altunbay, DoğanM.S