Masood, Khalid (2010) Histological image analysis and gland modelling for biopsy classification. PhD thesis, University of Warwick.

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Abstract

The area of computer-aided diagnosis (CAD) has undergone tremendous
growth in recent years. In CAD, the computer output is used
as a second opinion for cancer diagnosis. Development of cancer is a
multiphase process and mutation of genes is involved over the years.
Cancer grows out of normal cells in the body and it usually occurs
when growth of the cells in the body is out of control. This phenomenon
changes the shape and structure of the tissue glands.
In this thesis, we have developed three algorithms for classification of
colon and prostate biopsy samples. First, we computed morphological
and shape based parameters from hyperspectral images of colon
samples and used linear and non-linear classifiers for the identification
of cancerous regions. To investigate the importance of hyperspectral
imagery in histopathology, we selected a single spectral band from
its hyperspectral cube and performed an analysis based on texture
of the images. Texture refers to an arrangement of the basic constituents
of the material and it is represented by the interrelationships
between the spatial arrangements of the image pixels. A novel feature
selection method based on the quality of clustering is developed
to discard redundant information. In the third algorithm, we used
Bayesian inference for segmentation of glands in colon and prostate
biopsy samples. In this approach, glands in a tissue are represented
by polygonal models with variuos number of vertices depending on
the size of glands. An appropriate set of proposals for Metropolis-
Hastings-Green algorithm is formulated and a series of Markov chain
Monte Carlo (MCMC) simulations are run to extract polygonal models
for the glands. We demonstrate the performance of 3D spectral
and spatial and 2D spatial analyses with over 90% classification accuracies
and less than 10% average segmentation error for the polygonal
models.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > R Medicine (General)
Library of Congress Subject Headings (LCSH):	Diagnosis -- Methodology, Diagnosis -- Computer programs, Cancer -- Diagnosis, Pattern recognition systems, Image analysis
Official Date:	2010
Dates:	Date Event 2010 Submitted
Institution:	University of Warwick
Theses Department:	Department of Computer Science
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Rajpoot, Nasir M. (Nasir Mahmood)
Extent:	xiii, 180 leaves : ill., charts
Language:	eng
URI:	http://wrap.warwick.ac.uk/id/eprint/3918

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