Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.694402
Title: An investigation of automatic processing techniques for time-lapse microscope images
Author: Li, Yuexiang
ISNI:       0000 0004 5991 3584
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2016
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Abstract:
The analysis of time-lapse microscope images is a recent popular research topic. Processing techniques have been employed in such studies to extract important information about cells—e.g., cell number or alterations of cellular features—for various tasks. However, few studies provide acceptable results in practical applications because they cannot simultaneously solve the core challenges that are shared by most cell datasets: the image contrast is extremely low; the distribution of grey scale is non-uniform; images are noisy; the number of cells is large, etc. These factors also make manual processing an extremely laborious task. To improve the efficiency of related biological analyses and disease diagnoses. This thesis establishes a framework in these directions: a new segmentation method for cell images is designed as the foundation of an automatic approach for the measurement of cellular features. The newly proposed segmentation method achieves substantial improvements in the detection of cell filopodia. An automatic measuring mechanism for cell features is established in the designed framework. The measuring component enables the system to provide quantitative information about various cell features that are useful in biological research. A novel cell-tracking framework is constructed to monitor the alterations of cells with an accuracy of cell tracking above 90%. To address the issue of processing speed, two fast-processing techniques have been developed to complete edge detection and visual tracking. For edge detection, the new detector is a hybrid approach that is based on the Canny operator and fuzzy entropy theory. The method calculates the fuzzy entropy of gradients from an image to decide the threshold for the Canny operator. For visual tracking, a newly defined feature is employed in the fast-tracking mechanism to recognize different cell events with tracking accuracy: i.e., 97.66%, and processing speed, i.e., 0.578s/frame.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.694402  DOI: Not available
Keywords: TA Engineering (General). Civil engineering (General) ; TA1501 Applied optics. Phonics ; TK Electrical engineering. Electronics Nuclear engineering
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