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Permanent link (DOI): https://doi.org/10.7939/R34747079

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Computer-aided Analysis of Whole Slide Skin Histopathological Images Open Access

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Other title
Subject/Keyword
Computer-aided Analysis
Skin
Histopathological Images
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Lu, Cheng
Supervisor and department
Jha, Naresh (Cross Cancer Institute)
Mandal, Mrinal (Electrical and Computer Engineering)
Examining committee member and department
Zemp, Roger (Electrical and Computer Engineering)
Mandal, Mrinal (Electrical and Computer Engineering)
Jha, Naresh (Cross Cancer Institute)
Ray, Nilanjan (Computing Science)
Krishnan, Sri (Electrical and Computer Engineering, Ryerson University)
Zhao, Vicky Hong (Electrical and Computer Engineering)
Department
Department of Electrical and Computer Engineering
Specialization
Digital Signals and Image Processing
Date accepted
2013-08-13T09:49:27Z
Graduation date
2013-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
The histopathological examination of a biopsy is considered as the gold standard in the diagnosis of diseases for almost all kinds of cancer. Traditionally, the histopathological slides are examined under a microscope by pathologists. Nowadays, with the help of high speed, high resolution image scanning technique, a glass slide can be digitized at high magnification to create a digital whole slide image (WSI). Manual examination of the glass slides and the WSIs are time-consuming and difficult. Also, the traditional diagnosis is subjective and often leads to intra-observer and inter-observer variability. In this dissertation, I develop several key techniques of the computer-aided diagnosis~(CAD) system for digital histopathological image analysis of skin specimen of melanocytic disease. This CAD system operates on reliable quantitative measures and provides objective and reproducible information that can be used by pathologist for diagnosis. The proposed CAD system has six modules. In the first module, the whole slide skin image is automated segmented into biologically meaningful parts: epidermis and dermis. The high resolution image tiles of interest are then generated for further analysis. In the second module, the nuclei in the epidermis area are segmented using the proposed hybrid gray-scale morphological reconstructions and local region adaptive threshold selection methods. In the third module, two efficient techniques based on local double ellipses descriptor analysis and radial line scanning analysis are proposed to detect the melanocytes. In the fourth module, an efficient technique is proposed to detect the mitotic cells in the multi-spectral histopathological images. Based on the pre-segmented regions of interest(ROI), the morphological features and the spatial relationship are analyzed in the fifth module. These features reveal the cytological and architectural characteristics of the tissue sample that are correlated to the disease diagnosis. In the last module, classification is performed using the pre-extracted features in order to grade the skin tissue. The experimental results based on a set of skin WSIs show that the proposed CAD system is able to provide objective and reproducible measures that can assist to the final diagnosis by pathologist.
Language
English
DOI
doi:10.7939/R34747079
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Cheng Lu, Muhammad Mahmood, Naresh Jha, Mrinal Mandal. “A Robust Automatic Nuclei Segmentation Technique for Quantitative Histopathological Image Analysis ” Analytical and Quantitative Cytology and Histopathology. December 2012, P. 296-308.Cheng Lu, Muhammad Mahmood, Naresh Jha, Mrinal Mandal. “Detection of Melanocytes in Skin Histopathological Images using Radial Line Scanning”, Pattern Recognition. Volume 46, Issue 2, February 2013, P. 509-518.Cheng Lu, Muhammad Mahmood, Naresh Jha, Marinal Mandal. “Automated Segmentation of the Melanocytes in Skin Histopathological Images”, IEEE Journal of Biomedical and Health Informatics. Vol. 17, NO. 2, March 2013, P. 284-296.Cheng Lu, Mrinal Mandal. ”Automated Segmentation and Analysis of the Epidermis Area in Skin Histopathological Images”, In Proc: The 34th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC 2012). August 2012, P. 5355-5359.Cheng Lu, Mrinal Mandal, “Towards Automatic Mitotic Cells Detection and Segmentation in Multi-spectral Histopathological Images”, IEEE Journal of Biomedical and Health Informatics. Accepted.Cheng Lu, Mrinal Mandal, “Automated Analysis and Diagnosis of Skin Melanoma on Whole Slide Histopathological Images”, submitted to Pattern Recognition.

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