Browsing by Subject "Automated cancer diagnosis and grading"

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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
Histopathological image classification using salient point patterns
(2011) Çığır, Celal
Over the last decade, computer aided diagnosis (CAD) systems have gained great importance to help pathologists improve the interpretation of histopathological tissue images for cancer detection. These systems offer valuable opportunities to reduce and eliminate the inter- and intra-observer variations in diagnosis, which is very common in the current practice of histopathological examination. Many studies have been dedicated to develop such systems for cancer diagnosis and grading, especially based on textural and structural tissue image analysis. Although the recent textural and structural approaches yield promising results for different types of tissues, they are still unable to make use of the potential biological information carried by different tissue components. However, these tissue components help better represent a tissue, and hence, they help better quantify the tissue changes caused by cancer. This thesis introduces a new textural approach, called Salient Point Patterns (SPP), for the utilization of tissue components in order to represent colon biopsy images. This textural approach first defines a set of salient points that correspond to nuclear, stromal, and luminal components of a colon tissue. Then, it extracts some features around these salient points to quantify the images. Finally, it classifies the tissue samples by using the extracted features. Working with 3236 colon biopsy samples that are taken from 258 different patients, our experiments demonstrate that Salient Point Patterns approach improves the classification accuracy, compared to its counterparts, which do not make use of tissue components in defining their texture descriptors. These experiments also show that different set of features can be used within the SPP approach for better representation of a tissue image.