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Title: Model based system for automated analysis of biomedical images
Author: Aguilar Chongtay, María del Rocío
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1997
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This thesis is concerned with developing a probabilistic formulation of model-based vision using generalised flexible template models. It includes the design and implementation of a system which extends flexible template models to include grey level information in the object representation for image interpretation. This system was designed to deal with microscope images where the different stain and illumination conditions during the image acquisition process produce a strong correlation between density profile and geometric shape. This approach is based on statistical knowledge from a training set of examples. The variability of the shape-grey level relationships is characterised by applying principal component analysis to the shape-grey level vector extracted from the training set. The main modes of variation of each object class are encoded with a generic object formulation constrained by the training set limits. This formulation adapts to the diversity and irregularities of shape and view during the object recognition process. The modes of variation are used to generate new object instances for the matching process of new image data. A genetic algorithm method is used to find the best possible explanation for a candidate of a given model, based on the probability distribution of all possible matches. This approach is demonstrated by its application to microscope images of brain cells. It provides the means to obtain information such as brain cells density and distribution. This information could be useful in the understanding of the development and properties of some Central Nervous System (CNS) related diseases, such as in studies on the effects of HIV in CNS where neuronal loss is expected. The performance of the SGmodel system was compared with manual neuron counts from domain experts. The results show no significant difference between SGmodel and manual neuron estimates. The observation of bigger differences between the counts of the domain experts underlines the automated approach importance to perform an objective analysis.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available