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Uterus Segmentation in Dynamic MRI using LBP texture descriptors

机译:使用LBP纹理描述符的动态MRI子宫分割

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Pelvic floor disorders cover pathologies of which physiopathology is not well understood. However cases get prevalent with an ageing population. Within the context of a project aiming at modelization of the dynamics of pelvic organs, we have developed an efficient segmentation process. It aims at alleviating the radiologist with a tedious one by one image analysis. From a first contour delineating the uterus-vagina set, the organ border is tracked along a dynamic mri sequence. The process combines movement prediction, local intensity and texture analysis and active contour geometry control. Movement prediction allows a contour intitialization for next image in the sequence. Intensity analysis provides image-based local contour detection enhanced by local binary pattern (lbp) texture descriptors. Geometry control prohibits self intersections and smoothes the contour. Results show the efficiency of the method with images produced in clinical routine.
机译:骨盆底障碍涵盖了对其病理生理学尚不十分了解的病理学。但是,随着人口老龄化,这种情况越来越普遍。在一个旨在对盆腔器官动力学进行建模的项目的背景下,我们开发了一种有效的分割过程。其目的是通过单调乏味的图像分析来减轻放射科医生的负担。从描绘子宫-阴道组的第一个轮廓开始,沿着动态mri序列跟踪器官边界。该过程结合了运动预测,局部强度和纹理分析以及主动轮廓几何控制。运动预测允许序列中下一个图像的轮廓初始化。强度分析提供了基于图像的局部轮廓检测,并通过局部二进制图案(lbp)纹理描述符进行了增强。几何控制禁止自相交并平滑轮廓。结果显示了该方法在临床常规程序中产生图像的有效性。

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