Novel methods and applications in Computer Aided Medical Diagnosis

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Computer aided medical diagnosis (mCAD) is a rapidly developing field of investigation offering improved diagnosis and quantitative image analysis solutions for clinicians, helping to increase specificity and diagnostic confidence. mCAD systems can be applied to all imaging modalities for accurate lesion detection, treatment planning and serial monitoring of the patients. Feature extraction and analysis is a core element of signal and image processing systems encountered in mCAD. The key to successful design of such a system for human-machine interaction depends on a robust feature extraction and classification systems which is the main topics of our investigation. Our goal is to develop a computer aided medical decision support system for disease identification and classification in pediatric patients. Pediatric patients often have a variety of usual and uncommon disorders not encountered in adults such as metabolic brain diseases (MBD). Compared to the adult patient population, pediatric patients also represent unique challenges for diagnostic imaging due to their smaller size, anatomical variability and altered motion. We propose a novel computer aided methodology to assist radiologists in early and accurate diagnosis of pediatric metabolic brain diseases. To our knowledge, our approach introduces a new era in medical diagnostics and computer aided support systems by infusing image and signal information obtained from two different imaging modalities for enhanced diagnosis accuracy, where each modality by itself may not always be sufficient. Additionally, while our proposed approach focuses on metabolic brain disease for the purpose of this grant application, the resultant methodologies will significantly contribute to the advancement of biomedical signal and image analysis and pattern recognition extensible to other disorders. Our proposed mCAD scheme could also have immense potential in the intelligent systems and information technology sectors. We are collaborating with the Department of Diagnostic Imaging at SickKids in Toronto. The MSc and PhD trainees will be provided with a unique opportunity to collaborate in this multidisciplinary environment.