首页> 美国卫生研究院文献>Heliyon >A hybrid region growing tumour segmentation method for low contrast and high noise Nuclear Medicine (NM) images by combining a novel non-linear diffusion filter and global gradient measure (HNDF-GGM-RG)
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A hybrid region growing tumour segmentation method for low contrast and high noise Nuclear Medicine (NM) images by combining a novel non-linear diffusion filter and global gradient measure (HNDF-GGM-RG)

机译:结合新型非线性扩散滤波器和全局梯度测度(HNDF-GGM-RG)的低对比度和高噪声核医学(NM)图像的混合区域生长肿瘤分割方法

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摘要

Poor spatial resolution and low signal-to-noise ratio (SNR) along with the finite image sampling constraint make lesion segmentation on Nuclear Medicine (NM) images (e.g., PET–Positron Emission Tomography) a challenging task. Since the size, signal-to-background ratio (SBR) and SNR of lesion vary within and between patients, performance of the conventional segmentation methods are not consistent against statistical fluctuations. To overcome these limitations, a hybrid region growing segmentation method is proposed combining non-linear diffusion filter and global gradient measure (HNDF-GGM-RG). The performance of the algorithm is validated on PET images and compared with the 40%-fixed threshold and a state-of-the-art active contour (AC) methods. Segmented volume, dice similarity coefficient (DSC) and percentage classification error (% CE) were used as the quantitative figures of merit (FOM) using the torso NEMA phantom that contains six different sizes of spheres. A 2:1 SBR was created between the spheres and background and the phantom was scanned with a Siemens TrueV PET-CT scanner. 40T method is SNR dependent and overestimates the volumes . AC volumes match with the true volumes only for the largest three spheres. On the other hand, the proposed HNDF-GGM-RG volumes match closely with the true volumes irrespective of the size and SNR. Average DSC of 0.32 and 0.66 and % CE of 700% and 160% were achieved by the 40T and AC methods respectively. Conversely, average DSC and %CE are 0.70 and 60% for HNDF-GGM-RG and less dependent on SNR. Since two-sample t-test indicates that the performance of AC and HNDF-GGM-RG are statistically significant for the smallest three spheres and similar for the rest, HNDF-GGM-RG can be applied where the size, SBR and SNR are subject to change either due to alterations in the radiotracer uptake because of treatment or uptake variability of different radiotracers because of differences in their molecular pathways.
机译:较差的空间分辨率和较低的信噪比(SNR)以及有限的图像采样限制,使得在核医学(NM)图像(例如PET-正电子发射断层扫描)上进行病变分割成为一项艰巨的任务。由于病变的大小,信噪比(SBR)和患者内和患者之间的信噪比(SNR)有所不同,因此常规分割方法的性能与统计波动不一致。为了克服这些限制,提出了一种结合非线性扩散滤波器和全局梯度测度(HNDF-GGM-RG)的混合区域增长分割方法。该算法的性能在PET图像上得到了验证,并与40%固定阈值和最新的主动轮廓(AC)方法进行了比较。使用包含六个不同大小球体的NEMA躯干模型,将分割的体积,骰子相似系数(DSC)和百分比分类误差(%CE)用作定量品质因数(FOM)。在球和背景之间创建了2:1的SBR,并使用Siemens TrueV PET-CT扫描仪扫描了幻像。 40T方法取决于SNR,并高估了体积。 AC体积仅与最大的三个球体的真实体积匹配。另一方面,无论大小和SNR如何,建议的HNDF-GGM-RG体积都与真实体积紧密匹配。通过40T和AC方法分别获得0.32和0.66的平均DSC和700%和160%的%CE。相反,对于HNDF-GGM-RG,平均DSC和%CE分别为0.70和60%,并且对SNR的依赖性较小。由于两次样本t检验表明AC和HNDF-GGM-RG的性能在最小的三个球体上具有统计显着性,而其余的相似,因此HNDF-GGM-RG可以应用于大小,SBR和SNR受其影响的地方由于治疗引起的放射性示踪剂吸收改变或由于分子途径不同而导致的不同放射性示踪剂吸收变化而改变。

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