Human Lesion Detection Method Based on Image Information and Brain Signal
Human Lesion Detection Method Based on Image Information and Brain Signal
复制标题
基于图像信息和脑信号的人体病变检测方法
DOI：
10.1109/access.2019.2891749
复制
发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Manogaran, Gunasekaran
中科院分区：
文献类型：
--
作者：
Li, Gongfa;Jiang, Du;Manogaran, Gunasekaran
大脑是中枢神经系统中最大、最复杂的结构。它支配着人体的所有活动，人体内的损伤也反映在大脑信号中。本文采用图像的方法来辅助脑信号检测人体的病变。由于医学图像的特殊性，对任何一幅医学图像都没有通用的分割方法，也没有客观的标准来判断分割是否有效。医学图像分割技术仍然是制约其他相关技术在医学图像处理中发展和应用的瓶颈。基于以上原因，本文提出了一种基于模糊理论和区域生长算法的改进区域生长算法。该算法被用于分割人体不同器官的肝脏和胸部X射线的医学图像。改进后的算法采用阈值分割算法辅助种子点的自动选取，并改进了区域生长规则，然后利用形态后处理来提高分割效果。实验结果表明，改进的区域生长算法在两个不同器官下均有较好的分割效果，证明该算法具有一定的适用性，其分割精度和分割质量均优于传统的区域生长算法。该算法结合了阈值方法和传统区域生长方法的优点。在算法上是可行的，具有一定的应用价值。
The brain is the largest and most complex structure in the central nervous system. It dominates all activities in the body, and the lesions in the human body are also reflected in the brain signal. In this paper, the image method is used to assist the brain signal to detect the human lesion. Due to the particularity of medical images, there is no common segmentation method for any medical image, and there is no objective standard to judge whether the segmentation is effective. Medical image segmentation technology is still a bottleneck restricting the development and the application of other related technologies in medical image processing. Based on the above reasons, this paper proposes an improved region growing algorithm based on the fuzzy theory and region growing algorithm. The algorithm is used to segment the medical images of the liver and chest X-ray of different human organs. The improved algorithm uses a threshold segmentation algorithm to assist in the automatic selection of seed points and improves the region growing rules, then morphological post-processing is used to improve the segmentation effect. The experimental results show that the improved region growing algorithm has better segmentation effect under two different organs, which proves that the algorithm has certain applicability, and its accuracy and segmentation quality are better than the traditional region growing algorithm. This algorithm combines the advantages of the threshold method and traditional region growing method. It is feasible in algorithm and has certain application value.