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Automatic detection and segmentation of brain tumor using fuzzy classification and deformable models

机译:使用模糊分类和变形模型自动检测和分割脑肿瘤

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We propose a new general method for segmenting brain tumors in 3D magnetic resonance images. Our method is applicable to different types of tumors. First, the brain is segmented using a new approach, robust to the presence of tumors. Then a tumor detection is performed, based on improved fuzzy classification. Its result constitutes the initialization of a segmentation method based on a deformable model, leading to a precise segmentation of the tumors. Imprecision and variability are taken into account at all levels, using appropriate fuzzy models. The result obtained on different types of tumors have been evaluated by comparison with manual segmentations.
机译:我们提出了一种在3D磁共振图像中分割脑肿瘤的新通用方法。我们的方法适用于不同类型的肿瘤。首先,使用一种新的方法对大脑进行分割,这种方法对存在的肿瘤具有鲁棒性。然后,基于改进的模糊分类,执行肿瘤检测。其结果构成了基于可变形模型的分割方法的初始化,从而导致了肿瘤的精确分割。使用适当的模糊模型,在所有级别都考虑了不精确性和可变性。通过与手动分割进行比较,评估了在不同类型的肿瘤上获得的结果。

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