使用机器学习和保形几何代数的脑肿瘤检测和三维建模

Soumya S Pillai, R. K. Megalingam
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引用次数: 3

摘要

医学成像或处理涉及范围广泛的计算,分析的准确性最依赖于这些计算。因此,为了提高分析的准确性,需要减少计算几何问题。问题通常发生在图像分割、形状逼近、三维建模和体数据配准过程中。共形几何代数是解决所有这些问题的有效范例。我们在MRI扫描后获得的图像是二维图像,必须从中识别肿瘤。本文为识别肿瘤在脑内区域的生长提供了关键,并为未来肿瘤的三维建模提供了参考,这可能有助于医生有效地治疗受肿瘤影响的患者。为了准确预测脑相关区域是否存在肿瘤,必须首先获取二维图像进行去噪。
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Detection and 3D Modeling of Brain Tumor using Machine learning and Conformal Geometric Algebra
Medical imaging or processing involves a wide range of computations on which the accuracy of analysis depends on the most. So to enhance the accuracy of analysis we need to reduce the computational geometric problems. Usually problems occur during the image segmentation or shape approximation or 3D modeling, and volumetric data registration. Con-formal geometric algebra is an effective paradigm to all this problems. The images we obtain after the MRI scan is 2D image from which the tumor has to be identified. This paper provides a key to identify the growth of the tumor in the regions inside the brain and to develop a 3D modeling of the tumor for the future reference which may help the doctor in the treatment of the tumor affected patient effectively. For the accurate prediction of whether a tumor is there in the brain related regions or not 2D image obtained must be first taken for the noise removal.
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