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The Investigation Study on Non-Linear Filter based Preprocessing for MRI Image Segmentation and Classification

机译:基于非线性滤波器的PRI图像分割和分类的研究研究

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Imaging techniques assists the medical practitioners and researchers to diagnose the activities and disorders in human body before persistent surgeries. Among several medical imaging modalities, magnetic resonance imaging provides additional contrast information about the tissues. Magnetic resonance imaging scans are used as significant method for identifying diseases throughout the human body. MRI scan provides sufficient information for patient diagnosis. Three processes namely preprocessing, segmentation and classification are performed on MRI images for finding the existence of disease. Many researchers introduced segmentation and classification techniques for improving the performance of tumor identification with MRI images. But, PSNR, segmentation time and classification accuracy performance of existing techniques was not improved. In order to address these problems, machine learning and deep learning based non-linear teager filter can be used in our research work.
机译:成像技术有助于医生和研究人员在持久的手术前诊断人体中的活动和障碍。 在若干医学成像模式中,磁共振成像提供有关组织的额外对比度信息。 磁共振成像扫描用作识别整个人体疾病的重要方法。 MRI扫描提供足够的患者诊断信息。 三个过程即预处理,分割和分类是对发现疾病存在的影响。 许多研究人员介绍了用于提高MRI图像的肿瘤鉴定性能的分段和分类技术。 但是,现有技术的PSNR,分割时间和分类准确性性能没有得到改善。 为了解决这些问题,在我们的研究工作中可以使用机器学习和基于深度学习的非线性茶叶过滤器。

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