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Title: Human and computer analysis of decompression illness using Tc-99m HMPAO-SPET
Author: Dickson, John Caddell
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 1998
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Decompression illness is one of the more serious hazards associated with diving. Although many of the symptoms of this condition are clearly recognisable, there is growing concern that decompression illness may cause sub-clinical long-term neuro-logical damage in some instance. One of the most promising methods to assess this damage is Tc-99m HMPAO-SPET imaging of the brain, however there has been un-certainty about the usefulness of this technique. The aim of this thesis is to assess the ability of Tc-99m HMPAO-SPET to identify neurological residua from decompression illness, using both human and computer methods of image analysis. Decompression illness is a multi-focal and diffuse disease, which, in the opinion of this centre leads to Tc-99m HMPAO-SPET images which have a globally patchy appearance. Using this premise, the work in this thesis uses ROC analysis with human observers, methods of texture analysis, and Statistical Parametric Mapping to assess differences in the global appearance of Tc-99m HMPAO-SPET images of decompression illness and non-diving controls. In addition, the importance of the time of imaging decompression illness is also assessed. The results of these analyses showed that clear differences exist between the image appearance of Tc-99m HMPAO-SPET images of decompression illness and non-diving control subjects, with SPM confirming that the disease is multi-focal and diffuse in nature. Experienced human observers were the most adept at distinguishing between the two groups, although methods of texture analysis did show a similar level of performance. The time of imaging was also found to be important with the differences between control and decompression illness imaging studies most marked between 8 and 14 months after treatment of the condition.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Medicine Medicine Pattern recognition systems Pattern perception Image processing Biomedical engineering Biochemical engineering