Local object patterns for tissue image representation and cancer classification
Author
Olgun, Gülden
Advisor
Demir, Çiğdem Gündüz
Date
2013Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
Histopathological examination of a tissue is the routine practice for diagnosis and
grading of cancer. However, this examination is subjective since it requires visual
interpretation of a pathologist, which mainly depends on his/her experience and
expertise. In order to minimize the subjectivity level, it has been proposed to use
automated cancer diagnosis and grading systems that represent a tissue image
with quantitative features and use these features for classifying and grading the
tissue. In this thesis, we present a new approach for effective representation and
classification of histopathological tissue images. In this approach, we propose to
decompose a tissue image into its histological components and introduce a set
of new texture descriptors, which we call local object patterns, on these components
to model their composition within a tissue. We define these descriptors
using the idea of local binary patterns. However, we define our local object pattern
descriptors at the component-level to quantify a component, as opposed to
pixel-level local binary patterns, which quantify a pixel by constructing a binary
string based on relative intensities of its neighbors. To this end, we specify neighborhoods
with different locality ranges and encode spatial arrangements of the
components within the specified local neighborhoods by generating strings. We
then extract our texture descriptors from these strings to characterize histological
components and construct the bag-of-words representation of an image from the
characterized components. In this thesis, we use two approaches for the selection
of the components: The first approach uses all components to construct a bag-ofwords
representation whereas the second one uses graph walking to select multiple
subsets of the components and constructs multiple bag-of-words representations
from these subsets. Working with microscopic images of histopathological colon
tissues, our experiments show that the proposed component-level texture descriptors
lead to higher classification accuracies than the previous textural approaches.
Keywords
Digital pathologyTissue image representation
Classification,
Texture
Local patterns
Graph walks
Colon cancer