Multilevel cluster ensembling for histopathological image segmentation
Author(s)
Advisor
Aykanat, CevdetDate
2011Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
In cancer diagnosis and grading, histopathological examination of tissues by
pathologists is accepted as the gold standard. However, this procedure has observer
variability and leads to subjectivity in diagnosis. In order to overcome such
problems, computational methods which use quantitative measures are proposed.
These methods extract mathematical features from tissue images assuming they
are composed of homogeneous regions and classify images. This assumption is
not always true and segmentation of images before classification is necessary.
There are methods to segment images but most of them are proposed for generic
images and work on the pixel-level. Recently few algorithms incorporated medical
background knowledge into segmentation. Their high level feature definitions
are very promising. However, in the segmentation step, they use region growing
approaches which are not very stable and may lead to local optima.
In this thesis, we present an efficient and stable method for the segmentation
of histopathological images which produces high quality results. We use existing
high level feature definitions to segment tissue images. Our segmentation method
significantly improves the segmentation accuracy and stability, compared to existing
methods which use the same feature definition. We tackle image segmentation
problem as a clustering problem. To improve the quality and the stability
of the clustering results, we combine different clustering solutions. This approach
is also known as cluster ensembles. We formulate the clustering problem as a
graph partitioning problem. In order to obtain diverse and high quality clustering
results quickly, we made modifications and improvements on the well-known
multilevel graph partitioning scheme. Our method clusters medically meaningful
components in tissue images into regions and obtains the final segmentation. Experiments showed that our multilevel cluster ensembling approach performed
significantly better than existing segmentation algorithms used for generic
and tissue images. Although most of the images used in experiments, contain
noise and artifacts, the proposed algorithm produced high quality results.
Keywords
Histopathological image segmentationCluster ensembles
Multilevel graph partitioning
Unsupervised segmentation