Segmentation and extraction of regions of interest for automated detection of anomalies in clinical thermal infrared images

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Herry, Christophe L.




This doctoral thesis presents a complete framework for automated detection of anomalies in clinical thermal IR images. Within that framework, it addresses two fundamental problems: the automated segmentation of a main object from the background; and the automated extraction of relevant, potentially abnormal Regions of Interest. A first segmentation approach called cued morphological processing of edge maps (CMpEm) is introduced, that uses a minimum amount of a priori information about a region to constrain the morphological processing of edge components. It is shown to outperform existing segmentation methods, especially when dealing with faint regions in challenging conditions. A second segmentation approach based on the classification of edge components is presented that is able to recover missing contours when other methods fail. A third segmentation strategy based on a rule-based fusion and morphological post-processing of segmented contours from different techniques is proposed and shown to improve significantly the performance of the global segmentation task. Furthermore, a new approach for automated extraction of anatomical Regions of Interest in clinical IR images is introduced. It is based on a robust landmark identification algorithm that produces more accurate landmark locations on smooth contours than state-of-the-art algorithms. Various aspects of the anomaly detection framework, including blind extraction of potentially abnormal Regions of Interest, are tested on a collection of thermal IR databases acquired in the course of this thesis, in order to demonstrate the key advantages of the framework.


Infrared imaging.
Infrared technology.
Infrared detectors.
Medical thermography.
Image analysis -- Mathematical models.
Expert systems (Computer science).
Imaging systems in medicine.
Image processing -- Digital techniques.




Carleton University

Thesis Degree Name: 

Doctor of Philosophy: 

Thesis Degree Level: 


Thesis Degree Discipline: 

Engineering, Electrical

Parent Collection: 

Theses and Dissertations

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