| 俞海平邬立保陈昌沉窦洪桥朱艳.基于动态方向梯度矢量流模型的脑肿瘤图像分割[J].,2012,12(6):1093-1097 |
| 基于动态方向梯度矢量流模型的脑肿瘤图像分割 |
| The Direction of Gradient Vector Flow Based on the Dynamic Model ofBrain Tumor Segmentation |
| |
| DOI： |
| 中文关键词: 脑肿瘤 图像分割 动态方向梯度矢量流模 |
| 英文关键词: Brain tumor Image segmentation Dynamic Directional Gradient Vector Flow Models |
| 基金项目: |
|
| 摘要点击次数: 930 |
| 全文下载次数: 1138 |
| 中文摘要: |
| 目的：针对GVF Snake 模型算法收敛容易陷入局部极小值及对初始轮廓位置敏感等缺点，提出一种动态方向梯度矢量流模
型(DDGVF)，使其更适合医学图像的分割。方法：利用主动轮廓模型的提取和跟踪特定区域内目标轮廓的方法，将其应用于医学
图像如CT、MRI 和超声图像的处理，以获取特定器官及组织的轮廓。结果：动态方向梯度矢量流场（DDGVF）能够较好地提取出
脑肿瘤图像。结论：利用该方法能够较好地分割提取出脑肿瘤图像的肿瘤病变区域，为进一步对其纹理和形状等特征进行描述和
分析提供了可靠的依据。 |
| 英文摘要: |
| Objective: Convergence of GVF Snake model for the algorithm is easy to fall into local minimum and the initial outline
of a position-sensitive and other shortcomings, propose a dynamic model of the direction of gradient vector flow (DDGVF), make it
more suitable for medical image segmentation. Method：Extraction using active contour model and track the target within a specific region
contour method, can be applied to medical imaging such as CT, MRI and ultrasound image processing, access to specific organs and
tissues of the contour. Result：This method can extract the images of brain tumors. Conclusion: It provides a reliable basis for further
study on their characteristics such as texture and shape description and analysis. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
