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A New Discrete Wavelet Transform Algorithm Based on Frame Theory and Its Application to Brain MRI Segmentation

机译:一种新的基于帧理论的离散小波变换算法及其对脑MRI分割的应用

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Automated brain tumor detection from MRI images is a very challenging job in today's modern medical imaging research. MRI is used to take image of soft tissues of human body. It is very helpful for analyzing human organs without surgical intervention. For automatic detection of tumor, segmentation of brain image is required. Segmentation partitions the image into distinct regions based on various parameters. It is the most important and challenging area of computer aided clinical diagnostic tools. Although Conventional segmentation approaches are computationally efficient, but have low quality of edge and feature detection. Here, we propose an algorithm on frame theoretic methods and the discrete wavelet transform and apply it to brain MRIs. Significant gains in performance are observed over conventional segmentation algorithms.
机译:来自MRI图像的自动脑肿瘤检测是当今现代医学成像研究中的一个非常具有挑战性的工作。 MRI用于拍摄人体软组织的形象。 在没有手术干预的情况下分析人器官是非常有帮助的。 为了自动检测肿瘤,需要进行脑图像的分割。 分割基于各种参数将图像分为不同区域。 这是计算机辅助临床诊断工具最重要和最具挑战性的领域。 虽然传统的分割方法是计算有效的,但具有低质量的边缘和特征检测。 在这里,我们提出了一种框架理论方法和离散小波变换的算法,并将其应用于脑MRIS。 在传统的分割算法上观察到性能显着提高。

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