基于分割和支持向量机的MRI图像脑肿瘤检测

Swapnil R. Telrandhe, Amit Pimpalkar, Ankita A. Kendhe
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引用次数: 69

摘要

在本文中我们提出了自适应脑肿瘤检测,图像处理是在医疗工具中用于检测肿瘤,只有MRI图像不能识别肿瘤区域,在本文中我们使用K-Means分割与图像预处理。其中采用了中值滤波和颅骨掩模去噪。此外,我们还使用目标标记来获得更详细的肿瘤区域信息。为了使这个系统具有适应性,我们使用了支持向量机(SVM),支持向量机以无监督的方式使用,它将用于创建和维护模式以备将来使用。对于模式,我们必须找出特征来训练支持向量机。为此,我们找出了纹理特征和颜色特征。预计该系统的实验结果将比其他现有系统得到更好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of brain tumor from MRI images by using segmentation & SVM
In this paper we propose adaptive brain tumor detection, Image processing is used in the medical tools for detection of tumor, only MRI images are not able to identify the tumorous region in this paper we are using K-Means segmentation with preprocessing of image. Which contains de-noising by Median filter and skull masking is used. Also we are using object labeling for more detailed information of tumor region. To make this system an adaptive we are using SVM (Support Vector Machine), SVM is used in unsupervised manner which will use to create and maintain the pattern for future use. Also for patterns we have to find out the feature to train SVM. For that here we have find out the texture feature and color features. It is expected that the experimental results of the proposed system will give better result in comparison to other existing systems.
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