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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
【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.
【Key words】 contrast enhancement; low-dose CT images; local statistic feature; computer-aided diagnosis;
- 【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