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Automatic MRI image segmentation using water flow like algorithm and fuzzy entropy

机译:像水流算法和模糊熵的MRI图像自动分割

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In this paper a new method is presented to improve the results of segmentation efficiency and accuracy. Uncertainty is an important issue in many aspects of image processing; and it must be handled in confrontation with noises and interpretative ambiguities during high level processing. Image fuzzification is one of the methods in this issue, and fuzzy entropy is the other benefits of fuzzy systems to increase reliability of segmentation. In order to provide an accurate method metaheuristic algorithms have effective results and used in numerous papers. In this paper a newer method is used, that is water flow like algorithm. The method proposed in this study can identify brain tumor fast. Input is a set of MRI slices of the patients, and output is cuts of the parts of the brain that contains a tumor that surrounded by a polygon. This method requires no image registration and it is an unsupervised technique.
机译:本文提出了一种新的方法来提高分割效率和准确性。在图像处理的许多方面,不确定性是一个重要问题。在进行高级处理时,必须面对噪声和解释性歧义。图像模糊化是本期中的方法之一,而模糊熵是模糊系统增加分割可靠性的另一个好处。为了提供一种准确的方法,元启发式算法具有有效的结果,并在众多论文中得到了应用。在本文中,使用了一种较新的方法,即像水流一样的算法。本研究提出的方法可以快速识别脑肿瘤。输入是一组患者的MRI切片,输出是包含被多边形包围的肿瘤的大脑部分的切开部分。该方法不需要图像配准,并且是无监督的技术。

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