Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.728825
Title: Quantitative analysis of TMA images using computer vision and machine learning approaches
Author: Yu, Haiyue
ISNI:       0000 0004 6496 6234
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2016
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Abstract:
In the research of cancer biomarker and cell signaling pathways, multiple immunofluorescence labelled tissue microarrays (TMAs) are often examined by pathologists. By looking at TMAs under microscopy, the pathologists can score the images visually. In order to improve the accuracy and reliability of the image scoring, an automatic quantification of TMA image is in high demand. This thesis presents the development and the validation of several key components of image analysis in order to automatically quantify TMA images. The first component is preprocessing of the image, the uneven illumination in fluorescent confocal microscopic images are corrected using a retrospective method and it is validated on this dataset. The second component is the design and validation of an original cell segmentation algorithm. This algorithm automatically segments cell nuclei and cell cytoplasm from the images. The validation of this algorithm shows more than 90% of accuracy subject to tissue images with good qualities. The quality of tissue images refers to the problems such as, clumpy and overlaping nuclei, DNA degradation, stromal cells and etc. As the tissue quality varies, the texture of tissue changes. The texture of tissue with different quality was analyzed and a classification model is built to classify the textures. The third component is an automatic tissue quality assessment algorithm for TMA images. In addition, an original framework is designed to assess the quality of TMA by automatically recognize different tissue types and produce the proportion of each type. This proportion of tissue types provides a quality metric for an image and it is shown in the study that this metric is useful for classify the tissue image quality. In conclusion, this thesis presents an original the pipeline including the three components, to analyze tissue microarray images. The validation of each method has shown good performance.
Supervisor: Noble, Julia Alison Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.728825  DOI: Not available
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