基于深度学习算法的脑肿瘤CT图像特征分割技术改进

针对基于数学形态的脑肿瘤CT图像特征分割技术存在准确率低、分割效果不明确的弊端,提出基于深度学习算法的脑肿瘤CT图像特征分割技术。将可视人体数据集CVH-2作为研究对象,对数据集中的图像实施预处理,对图像四个模态实施卷积分别获取不同模态彼此的差异信息,归一化获取脑肿瘤CT图像多模态3D-CNNs特征。对基于SAE深度学习算法的脑肿瘤CT图像特征分割模型实施二级训练,将脑肿瘤CT图像多模态3D-CNNs特征经过处理后获取的S,V通道数据输入模型实施训练,在第二级训练的过程中把第一级SAE训练得到的权重作为二级训练的原始权重,将一级训练中错误分割的组织结构和沟回作为二次训练的数据集,获取脑肿瘤CT图像特征的准确分割结果。实验结果表明,所提方法在脑肿瘤CT图像特征分割准确率和效率方面具有显著优势。

改进分水岭算法在脑肿瘤CT图像分割中的应用

传统的分水岭算法应用于脑肿瘤CT图像时容易产生过分割现象,为了避免该现象,提出一种改进的分水岭图像分割算法。该方法主要包含形态学重构以及标记控制思想,首先对原始梯度图像进行形态学重建,相当于滤波处理,然后对原图设定灰度阈值并进行形态学处理标记出肿瘤部分,接着用分水岭变换标记背景部分,最后根据两个标记部分对调整后的梯度图像进行强制最小和分水岭变换得到分割线。实验结果表明,该算法能够准确地区别肿瘤部分与背景,并用封闭的分割线包围脑肿瘤区域,从而避免了对不感兴趣区域的误分割,有较好的针对性。

Classification of Medical Brain Images

Since brain tumors endanger people’s living quality and even their lives, the accuracy of classification becomes more important. Conventional classifying techniques are used to deal with those datasets with characters and numbers. It is difficult, however, to apply them to datasets that include brain images and medical history (alphanumeric data), especially to guarantee the accuracy. For these datasets, this paper combines the knowledge of medical field and improves the traditional decision tree. The new classification algorithm with the direction of the medical knowledge not only adds the interaction with the doctors, but also enhances the quality of classification. The algorithm has been used on real brain CT images and a precious rule has been gained from the experiments. This paper shows that the algorithm works well for real CT data.