Constrained Delaunay triangulation for diagnosis and grading of colon cancer

Abstract

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.