Quantitative Analysis of Infrared Images

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Quantitative Analysis of Infrared Images
For Early Breast Cancer Detection

This thesis presents a quantitative method to analyze and evaluate infrared images to
improve early breast cancer detection. Researchers have demonstrated that breast
thermography, or infrared imaging, has great potential in early cancer detection and
prognosis indication. However, manual examinations of the thermograms for abnormal
hyperthermia and hyper-vascularity related to tumour growth can be highly subjective,
tedious and challenging if the differences between the normal and abnormal images are
subtle. To address the problem, this thesis proposes a method whereby statistical analysis
and image processing techniques are applied to extract thermal features that may be
relevant in discriminating the images. Specifically, algorithms were developed to extract
quantitative parameters to represent the vascular patterns of the thermal images. An
Artificial Neural Network is then employed to predict clinical outcomes based on the
extracted parameters and to identify the most relevant set of features for image


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