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Title: Algorithms for breast cancer grading in digital histopathology images
Author: Khan, Adnan M.
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2014
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Histological analysis of tissue biopsies by an expert pathologist is considered gold standard for diagnosing many cancers, including breast cancer. Nottingham grading system, which is the most widely used criteria for histological grading of breast tissues, consists of three components: mitotic count, nuclear atypia and tubular formation. In routine histological analysis, pathologists perform grading of breast cancer tissues by manually examining each tissue specimen against the three components, which is a laborious and subjective process and thus can suffer from low inter-observer agreement. With the advent of digital whole-slide scanning platforms, automatic image analysis algorithms can be used as a partial solution for these issues. The main goal of this dissertation is to develop frameworks that can aid towards building an automated or semi-automated breast cancer grading system. We present novel frameworks for detection of mitotic cells and nuclear atypia scoring in breast cancer histopathology images. Both of these frameworks can play a fundamental role in developing a computer-assisted breast cancer grading system. Moreover, the proposed image analysis frameworks can be adapted to grading and analysis of cancers of several other tissues such as lung and ovarian cancers. In order to deal with one of the fundamental problems in histological image analysis applications, we first present a stain normalisation algorithm that minimises the staining inconsistency in histological images. The algorithm utilises a novel image-specific colour descriptor which summarises the colour contents of a histological image. Stain normalisation algorithm is used in the remainder of the thesis as a preprocessing step. We present a mitotic cell detection framework mimicking a pathologist’s approach, whereby we first perform tumour segmentation to restrict our search for mitotic cells to tumour regions only, followed by candidate detection and evaluation in a statistical machine learning framework. We also employ a discriminative dictionary learning paradigm to learn the visual appearance of mitotic cells, that models colour, texture, and shape in a composite manner. Finally, we present a nuclear atypia scoring framework based on a novel image descriptor which summarises the texture heterogeneity, inherent in histological images in a compact manner. Classification is performed using a geodesic k-nearest neighbour classifier which explicitly exploits the structure of Riemannian manifold of the descriptor and achieves significant performance boost as compared to Euclidean counterpart.
Supervisor: Not available Sponsor: University of Warwick
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: RC0254 Neoplasms. Tumors. Oncology (including Cancer) ; TA Engineering (General). Civil engineering (General)