基于神经动力学的医学图像增强技术  

Medical image enhancement based on neural dynamics

作　　者：李熙莹[1] 黄镜荣[1] 蒲恬[1] 谭润初[1] 

机构地区：[1]中山大学物理科学与工程技术学院光电材料与技术国家重点实验室,广东广州510275

出　　处：《生物医学工程与临床》2006年第2期68-71,共4页Biomedical Engineering and Clinical Medicine

基　　金：广东省科技计划项目(编号:2003B30602);中山大学青年教师科研启动基金

摘　　要：目的探讨基于视觉神经元模型的图像增强算法在医学图像处理中的效果和自动实现的方便性。方法用基于视觉神经元ON-OFF模型的图像增强算法处理医学图像,实现对医学图像的自动增强;探讨增强算法的处理机制;分析衰减常数、增益系数和空间常数对图像增强处理的影响。结果选1例左额胶质瘤患者的T1W序列MR图像,1例右眶周及右额顶血管瘤患者的CTA图像。经图像增强处理,MR图像颅脑内部的结构更加清晰可见,图像层次丰富,组织边界分明,内容纹理层次丰富,易于观察。CTA图像处理后,血管瘤病变部位组织轮廓变得完整清晰,图像层次变得更加丰富。同时,整体对比度下降,图像视觉柔和。结论通过对大量医学图像的处理计算,证明选择适当的衰减常数、增益系数和空间常数,可以得到比较显著的图像增强效果。Objective To study the effect and application of the algorithm for medical image enhancement based on visual neural model. Methods The medical image was processed by the image enhancement algorithm based on visual neuron ONOFF model. The automatic enhancement for medical image was realized. The effect of passive decay rate constant, gain coefficient and space surround constant were analyzed. Results The T1W sequential MR imaging of 1 patient with left frontal glioma and CTA imaging of 1 patient with orbital and frontoparietal angioma were processed by imaging enhancement algorithm. As a result the MR and CTA images of intracranial structure and the tumor were more clear and easy to study. Conclusion After processing and calculating of a great deal of medical images,it demonstrates the better enhancement effect of images can be achieved by selecting of optimal passive decay rate constant, gain coefficient and space surround constant.

关 键 词：图像增强 医学图像 视觉神经元 ON—OFF模型 

分 类 号：R318[医药卫生—生物医学工程]