University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Shape and Texture Recognition for Automated Analysis of Pathology Images

Snell, Violet (2014) Shape and Texture Recognition for Automated Analysis of Pathology Images Doctoral thesis, University of Surrey.

[img]
Preview
Text
ThesisMain.pdf - Thesis (version of record)
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (4MB) | Preview
[img]
Preview
Text
Deposit Agreement.pdf - Thesis (version of record)
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (90kB) | Preview

Abstract

This research project is concerned with automated analysis of microscopic images used in clinical pathology for diagnosing disease. Application of computer vision methods can improve the accuracy, reliability and availability of tests, reduce the associated costs and ultimately improve patient outcomes. Three different areas of pathology are covered: 1. identification of clustered nuclei and detection of chromosomal abnormalities in DAPI-stained samples, 2. diagnosis of auto-immune diseases from indirect immuno fluorescence (IIF) images, and 3. detection of dividing nuclei in H&E stained histopathology sections. Despite the diversity of these application domains, the techniques used for their analysis are similar. For cluster identification in DAPI images we focus on object shape and extend existing methods of shape analysis with novel measurements of the boundary profile which detect notches between overlapping nuclei in a cluster. For abnormality detection we focus on texture and develop a novel decision-tree dictionary for patch quantisation. We continue to focus on texture for IIF images, developing suitable isotropic measurements as well as exploring the connections between classification of individual cells and whole patient samples. Detection of dividing cells in tissue sections requires a combined assessment of shape, texture and colour in order to fully represent all relevant facets of the object. Here we develop a method for stain normalisation which efficiently compensates for batch variations in stain strength and proportions, followed by a full pipe-line of segmentation, feature extraction and classification, resolving issues of class imbalance implicit in detection of rare objects. We develop an efficient and effective segmentation method, which is free of weight parameters and adaptable for use in different imaging modalities. We explore a variety of classifer types and ensemble structures, and suggest promising directions of future development in the broad application area of pathology image analysis.

Item Type: Thesis (Doctoral)
Divisions : Theses
Authors :
AuthorsEmailORCID
Snell, Violetviolet.snell@solent.ac.ukUNSPECIFIED
Date : 2014
Contributors :
ContributionNameEmailORCID
Thesis supervisorKittler, JosefUNSPECIFIEDUNSPECIFIED
UNSPECIFIEDChristmas, WilliamUNSPECIFIEDUNSPECIFIED
Depositing User : Melanie Hughes
Date Deposited : 28 Jul 2015 14:43
Last Modified : 28 Jul 2015 14:43
URI: http://epubs.surrey.ac.uk/id/eprint/808238

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800