ICT Research Center, Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing University, China.
J Xray Sci Technol. 2012;20(3):255-67. doi: 10.3233/XST-2012-0334.
Intensity inhomogeneity may cause considerable difficulties in segmentation of CT image. In order to overcome the difficulties caused by intensity inhomogeneity, the region-scalable fitting (RSF) model was put forward. RSF model draws upon intensity information in local regions with a controllable scale. But only using intensity information may lead to slow convergence rate and poor denoise ability. Combining the method of robust statistics, RSF model is improved in this paper. In the improved model, the intensity in RSF model is replaced with local robust statistics which is the weighted combination of inter-quartile range, mean absolute deviation and intensity median in local region. Inter-quartile range and mean absolute deviation in local region are introduced to sharpen object boundaries, and intensity median in local region is introduced to reduce image noise. The contrast experiments between RSF model and the improved model are provided, which demonstrate the fast convergence rate and robustness to noise of the improved model.
不均匀的强度可能会给 CT 图像分割带来很大的困难。为了克服强度不均匀带来的困难，提出了区域可扩展拟合（RSF）模型。RSF 模型利用具有可控尺度的局部区域的强度信息。但是，仅使用强度信息可能会导致收敛速度慢和去噪能力差。本文结合稳健统计方法，对 RSF 模型进行了改进。在改进的模型中，RSF 模型中的强度被替换为局部稳健统计量，这是局部区域中四分位距、平均绝对偏差和强度中位数的加权组合。局部区域中的四分位距和平均绝对偏差用于锐化目标边界，而局部区域中的强度中位数用于减少图像噪声。提供了 RSF 模型和改进模型之间的对比实验，结果表明改进模型具有快速收敛速度和对噪声的鲁棒性。
