Novel methods and applications in Computer Aided Medical Diagnosis

计算机辅助医学诊断的新方法及应用

• 批准号: 239007-2012
• 负责人: Alirezaie, Javad
• 金额: $ 1.31万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570547
• 关键词: Novel; methods; applications; Computer; Aided

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.

计算机辅助医疗诊断(MCAD)是一个发展迅速的研究领域 为临床医生提供诊断和定量图像分析解决方案，有助于提高特异性和诊断性 自信。MCAD系统可以应用于所有成像方式，以准确地检测、治疗病变 对患者进行计划和连续监测。特征提取和分析是信号处理的核心内容 在MCAD中遇到的图像处理系统。成功设计这样的人机交互系统的关键在于一个健壮的特征提取和分类系统，这是我们研究的主要课题。我们的目标是开发一个计算机辅助医疗决策支持系统，用于 儿科患者的疾病识别和分类。儿科患者通常有成人没有遇到的各种常见和不常见的疾病，如代谢性脑疾病(MBD)。与成人患者群体相比，儿科患者也对诊断成像提出了独特的挑战，因为他们 体积较小，解剖学上的可变性和运动改变。 我们提出了一种新的计算机辅助方法，以帮助放射科医生早期和准确地诊断 儿童代谢性脑疾病。据我们所知，我们的方法引入了医学的新纪元 诊断和计算机辅助支持系统通过输入从两个图像和信号信息 增强诊断准确性的不同成像模式，其中每种模式本身可能并不总是足够的。此外，虽然我们建议的方法侧重于代谢性脑疾病，但所产生的方法将大大有助于生物医学信号和图像分析以及可扩展到其他疾病的模式识别的进步。我们建议的MCAD计划在智能系统和信息技术领域也可能具有巨大的潜力。我们正在与多伦多SickKids的诊断成像部合作。硕士和博士实习生将获得在这个多学科环境中进行合作的独特机会。