Neurodegenerative disorders are commonly characterized by atrophy of the brain which is caused by neuronal loss. Ventricles are one of the prominent structures in the brain; their shape changes, due to their content, the cerebrospinal fluid. Analyzing the morphological changes of ventricles, aids in the diagnosis of atrophy, for which the region of interest needs to be separated from the background. This study presents a modified distance regularized level set evolution segmentation method, incorporating regional intensity information. The proposed method is implemented for segmenting ventricles from brain images for normal and atrophy subjects of magnetic resonance imaging and computed tomography images. Results of the proposed method were compared with ground truth images and produced sensitivity in the range of 65%–90%, specificity in the range of 98%–99%, and accuracy in the range of 95%–98%. Peak signal to noise ratio and structural similarity index were also used as performance measures for determining segmentation accuracy: 95% and 0.95, respectively. The parameters of level set formulation vary for different datasets. An optimization procedure was followed to fine tune parameters. The proposed method was found to be efficient and robust against noisy images. The proposed method is adaptive and multimodal.

中文翻译：

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修正的距离正则化水平集演化用于脑室分割

神经退行性疾病通常以神经元萎缩引起的大脑萎缩为特征。心室是大脑中的突出结构之一。由于它们的含量，它们的形状会改变，即脑脊液。分析心室的形态变化，有助于萎缩的诊断，为此需要将感兴趣区域与背景分开。这项研究提出了一种改进的距离正则化水平集演化分割方法，该方法结合了区域强度信息。所提出的方法被实施用于从磁共振成像和计算机断层摄影图像的正常和萎缩受试者的脑图像中分割脑室。将该方法的结果与地面真实图像进行比较，得出的灵敏度在65％–90％之间，特异性在98％–99％的范围内，准确度在95％–98％的范围内。峰信噪比和结构相似性指标也被用作确定分割精度的性能指标：分别为95％和0.95。水平集公式化的参数因不同的数据集而异。遵循优化程序来微调参数。发现所提出的方法对噪声图像是有效且鲁棒的。所提出的方法是自适应的和多峰的。发现所提出的方法对噪声图像是有效且鲁棒的。所提出的方法是自适应的和多峰的。发现所提出的方法对噪声图像是有效且鲁棒的。所提出的方法是自适应的和多峰的。