Brain tumor segmentation is routinely needed in diagnostic radiology. Automatic volume estimation has been used for diagnosis improvement and treatment process. This article presents a new approach for tumor volume estimation, using a variational level set method. The new adapted representation of intensity allows the method to be efficient in the region of interest identification, regardless the shape, size, and location of the tumor. For the estimation of an optimal bounding box, a fuzzy preference optimization model is used. The proposed approach is suitable for the zero level set initialization as well as for the reduction of the processing area, which ultimately speeds up the curve evolution. Moreover, tumor contours are determined using the hybrid level set technique, which combines the gradient and local phase information as an edge indicator term. Such an approach is robust to attenuation and intensity inhomogeneity. The proposed method is evaluated using a set of real and synthetic images. Our method achieved a performance of 96% accuracy, with an average execution time of 4.75 seconds. The proposed method is fast, accurate, and does not require training data or prior knowledge. With such experimental results, our approach outperforms 18 state-of-the-art methods.

中文翻译：

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低级别胶质瘤的自动三维检测和体积估计

诊断放射学中通常需要脑肿瘤分割。自动体积估计已用于诊断改进和治疗过程。本文提出了一种使用变分水平集方法估计肿瘤体积的新方法。无论肿瘤的形状、大小和位置如何，新的适应强度表示允许该方法在感兴趣区域识别中是有效的。对于最优边界框的估计，使用了模糊偏好优化模型。所提出的方法适用于零水平集初始化以及处理区域的减少，最终加速曲线演化。此外，使用混合水平集技术确定肿瘤轮廓，它结合了梯度和局部相位信息作为边缘指示项。这种方法对于衰减和强度不均匀性是稳健的。所提出的方法使用一组真实和合成图像进行评估。我们的方法实现了 96% 的准确率，平均执行时间为 4.75 秒。所提出的方法快速、准确，并且不需要训练数据或先验知识。有了这样的实验结果，我们的方法优于 18 种最先进的方法。