Segmentation of colon glands by object graphs
Author(s)
Advisor
Demir, Çiğdem GündüzDate
2008Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
Histopathological examination is the most frequently used technique for clinical
diagnosis of a large group of diseases including cancer. In order to reduce the
observer variability and the manual effort involving in this visual examination,
many computational methods have been proposed. These methods represent
a tissue with a set of mathematical features and use these features in further
analysis of the biopsy. For the tissue types that contain glandular structures, one
of these analyses is to examine the changes in these glandular structures. For
such analyses, the very first step is to segment the tissue into its glands.
In this thesis, we present an object-based method for the segmentation of
colon glands. In this method, we propose to decompose the image into a set of
primitive objects and use the spatial distribution of these objects to determine
the locations of glands. In the proposed method, pixels are first clustered into
different histological structures with respect to their color intensities. Then, the
clustered image is decomposed into a set of circular primitive objects (white
objects for luminal regions and black objects for nuclear regions) and a graph
is constructed on these primitive objects to quantify their spatial distribution.
Next, the features are extracted from this graph and these features are used to
determine the seed points of gland candidates. Starting from these seed points,
the inner glandular regions are grown considering the locations of black objects.
Finally, false glands are eliminated based on another set of features extracted from
the identified inner regions and exact boundaries of the remaining true glands are
determined considering the black objects that are located near the inner glandular
regions.
Our experiments on the images of colon biopsies have demonstrated that
our proposed method leads to high sensitivity, specificity, and accuracy rates.and that it greatly improves the performance of the previous pixel-based gland
segmentation algorithms. Our experiments have also shown that the object-based
structure of the method provides tolerance to artifacts resulting from variances
in biopsy staining and sectioning procedures. This proposed method offers an
infrastructure for further analysis of glands for the purpose of automated cancer
diagnosis and grading.
Keywords
Histopathological image analysisGland segmentation
Object-based segmentation
Object-graphs