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Method and system to detect the microcalcifications in X-ray images using nonlinear energy operator

机译:使用非线性能量算子检测X射线图像中微钙化的方法和系统

摘要

A method and system to detect the microcalcifications (MC) in different type of images viz. X-ray images/mammograms/computer tomography with varied densities using nonlinear energy operator (NEO) is disclosed to favor precise detection of early breast cancer. Such Microcalcifications are associated with both high intensity and high frequency content. The same NEO output is useful to detect and remove the irrelevant curvilinear structures (CLS) thereby helps in reducing the false alarms in micro calcification detection technique. This is effective on different dataset (scanned film, mammograms with large spatial resolution such as CR and DR) of varied breast composition (viz. dense, fatty glandular, fatty), demonstrated quantitatively by Free-response receiver operating characteristic (FROC). Importantly, the method and apparatus of the invention can be used in conjunction with machine learning techniques viz. SVM to favor detection of incipient or small microcalcifications, thus benefiting radiologists in confirming detection of micro-calcifications in X-rays images/mammograms and reducing death rates.
机译:一种检测不同类型图像中的微钙化(MC)的方法和系统。公开了使用非线性能量算子(NEO)的具有不同密度的X射线图像/乳房X线照片/计算机断层摄影术,以有利于早期乳腺癌的精确检测。这种微钙化与高强度和高频含量有关。相同的NEO输出可用于检测和去除不相关的曲线结构(CLS),从而有助于减少微钙化检测技术中的错误警报。这对不同的乳房组成(即致密,脂肪腺,脂肪)的不同数据集(扫描胶片,具有较大空间分辨率的乳房X线照片,例如CR和DR)有效,通过自由响应接收器操作特征(FROC)定量证明。重要的是,本发明的方法和设备可以与机器学习技术结合使用。 SVM支持检测初期或小的微钙化,因此使放射科医生受益于确认检测X射线图像/乳房X线照片中的微钙化并降低死亡率。

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