Browsing by Subject "Colon (Anatomy)--Cancer--Diagnosis."

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Open Access
Constrained Delaunay triangulation for diagnosis and grading of colon cancer
(2009) Erdoğan, Süleyman Tuncer
In our century, the increasing rate of cancer incidents makes it inevitable to employ computerized tools that aim to help pathologists more accurately diagnose and grade cancerous tissues. These mathematical tools offer more stable and objective frameworks, which cause a reduced rate of intra- and inter-observer variability. There has been a large set of studies on the subject of automated cancer diagnosis/grading, especially based on textural and/or structural tissue analysis. Although the previous structural approaches show promising results for different types of tissues, they are still unable to make use of the potential information that is provided by tissue components rather than cell nuclei. However, this additional information is one of the major information sources for the tissue types with differentiated components including luminal regions being useful to describe glands in a colon tissue. This thesis introduces a novel structural approach, a new type of constrained Delaunay triangulation, for the utilization of non-nuclei tissue components. This structural approach first defines two sets of nodes on cell nuclei and luminal regions. It then constructs a constrained Delaunay triangulation on the nucleus nodes with the lumen nodes forming its constraints. Finally, it classifies the tissue samples using the features extracted from this newly introduced constrained Delaunay triangulation. Working with 213 colon tissues taken from 58 patients, our experiments demonstrate that the constrained Delaunay triangulation approach leads to higher accuracies of 87.83 percent and 85.71 percent for the training and test sets, respectively. The experiments also show that the introduction of this new structural representation, which allows definition of new features, provides a more robust graph-based methodology for the examination of cancerous tissues and better performance than its predecessors.
Open Access
Local object patterns for tissue image representation and cancer classification
(2013) Olgun, Gülden
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.