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基于局部特征分析的LDCT增强算法

Novel Algorithm for Low-dose CT Image Enhancement Based on Local Feature Analysis

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【作者】 张国栋颜培玉赵宏

【Author】 ZHANG Guo-dong1,2,YAN Pei-yu2,ZHAO Hong11 (Software Center, Northeastern University, Shenyang 110004, China)2 (School of Computer Science, Shenyang Institute of Aeronautical Engineering, Shenyang 110136, China)

【机构】 东北大学软件中心沈阳航空工业学院计算机学院

【摘要】 针对CT影像灰度动态范围宽、对比度差的问题,提出一种基于局部特征分析的低剂量CT(Low-dose Computed Tomography,LDCT)影像增强算法.算法通过对影像全局和局部统计特征的分析,构造非线性变换函数,实现对活动子块所对应区域的局部动态范围拉伸.选用两组不同采集协议CT影像进行的对比实验表明,算法实现简捷,可有效增强影像中的细部解剖结构,并可较好地抑制LDCT影像中的线性伪影.算法已被用于肺癌计算机辅助诊断系统的预处理过程.

【Abstract】 In order to improve the intensity and contrast qualities of CT (Computed Tomography) images in clinic application, this paper proposed a fast and smart low-dose CT images contrast enhancement algorithm based on local feature analysis. The method based on creating active sub-blocks with analyzing the local static feature for each local region. Then, perform a local modified contrast stretching according to an adaptive transfer function within the image region corresponds with the sub-block. The experimental results of two sets CT images with different acquisition protocol (one with 200-250mA tube current, and another with 80mA) show that the proposed algorithm provides a flexible and efficient way for low-dose CT image enhancement, enhances the detail anatomic structure effectively, and constrains the linear artifacts in low-dose CT images preferably than general method. Now, the method is used as a pre-processing procedure in a Lung Cancer CAD system.

【基金】 国家自然科学基金项目(60441004,60671050)资助;沈阳市科学技术计划项目(1063297-1-00)资助;辽宁省教育厅科学研究计划项目(05L322)资助
  • 【文献出处】 小型微型计算机系统 ,Journal of Chinese Computer Systems , 编辑部邮箱 ,2008年12期
  • 【分类号】TP391.41
  • 【被引频次】1
  • 【下载频次】75
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