Automated region detection based on the contrast-to-noise ratio in near-infrared tomography.
Automated region detection based on the contrast-to-noise ratio in near-infrared tomography.
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DOI：
10.1364/ao.43.001053
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发表时间：
2004-02
期刊：
影响因子：
1.9
通讯作者：
Xiaomei Song;B. Pogue;Shudong Jiang;M. Doyley;H. Dehghani;T. Tosteson;K. Paulsen
中科院分区：
文献类型：
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作者：
Xiaomei Song;B. Pogue;Shudong Jiang;M. Doyley;H. Dehghani;T. Tosteson;K. Paulsen
对比度噪声比（CNR）被用来确定从漫射近红外断层扫描重建图像内的对象的可检测性。得出的结论是，有一个最大值的CNR的图像内的对象的位置附近，并从CNR的真实区域的大小可以估计。实验和模拟研究得出的结论是，可以自动检测与CNR分析的对象，我们目前的系统具有近4毫米的空间分辨率限制和近1.4的对比度分辨率限制。提出了一种新的线性卷积CNR计算方法，用于感兴趣区域（ROI）的自动检测。
The contrast-to-noise ratio (CNR) was used to determine the detectability of objects within reconstructed images from diffuse near-infrared tomography. It was concluded that there was a maximal value of CNR near the location of an object within the image and that the size of the true region could be estimated from the CNR. Experimental and simulation studies led to the conclusion that objects can be automatically detected with CNR analysis and that our current system has a spatial resolution limit near 4 mm and a contrast resolution limit near 1.4. A new linear convolution method of CNR calculation was developed for automated region of interest (ROI) detection.