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Shu, Jie (2014) Immunohistochemistry image analysis : protein, nuclei and gland. PhD thesis, University of Nottingham.

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Abstract

This thesis focus on the analysis of digitized microscopic image, especially on IHC stained colour images. The corresponding contributions focused on the automatic detection of stain colour and glands, the segmentation and quantification of cell nuclei, the analysis of liver cirrhosis and the development of a semi-automatic toolbox.

Colour is the most important feature in the analysis of immunostained images. We developed a statistical colour detection model for stain colour detection based on the histograms of collected colour pixels. This is acting on the approach "what you see is what you get" which outperforms the other methods on the detection of several kinds of stain colour.

Verifying the presence of nuclei and quantifying positive nuclei is the foundation of cancer grading. We developed a novel seeded nuclei segmentation method which greatly improves the segmentation accuracy and reduces both over-segmentation and under-segmentation. This method has been demonstrated to be robust and accurate in both segmentation and quantification against manual labelling and counting in the evaluation process.

The analysis of gland architecture, which reflects the cancer stage, has evolved into an important aspect of cancer detection. A novel morphology-based approach has been developed to segment gland structures in H-DAB stained images. This method locates the gland by focusing on its morphology and intensity characteristics, which covers variations in stain colours in different IHC images. The evaluation results have demonstrated the improvements of accuracy and efficiency.

For the successive development of three methods, we put them in a semi-automatic toolbox for the aid of IHC image analysis. It can detect different kinds of stain colour and the basic components in an IHC image. The user created models and parameters can be saved and transferred to different users for the reproduction of detection results in different laboratories.

To demonstrate the flexibility of our developed stained colour detection technique, the tool has been extended to the analysis of liver cirrhosis. It is a novel method based on our statistical colour detection model which greatly improves the analysis accuracy and reduces the time cost.

Item Type:	Thesis (University of Nottingham only) (PhD)
Supervisors:	Qiu, G. Ilyas, M.
Subjects:	Q Science > QA Mathematics > QA 75 Electronic computers. Computer science Q Science > QR Microbiology > QR180 Immunology
Faculties/Schools:	UK Campuses > Faculty of Science > School of Computer Science
Item ID:	27616
Depositing User:	Shu, Jie
Date Deposited:	18 Feb 2015 10:15
Last Modified:	13 Oct 2017 17:15
URI:	https://eprints.nottingham.ac.uk/id/eprint/27616

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