论著摘要 |【AI-PET/CT】全身PET和CT成像中,一种新的在NSCT域中基于自适应PCNN的融合框架(双语版)

2018-02-27 18:28:54 admin
标签:   人工智能 PET CT 影像融合 全身扫描 脉冲耦合神经网络 PCNN 修正拉普拉斯算子

A Novel Fusion Framework Based on Adaptive PCNN in NSCT Domain for Whole-Body PET and CT Images.

发表日期: 2017.04.03   来源:Comput Math Methods Med. 2017;2017:8407019.

作者:

Song Z1, Jiang H1, Li S1.

作者介绍:

1. Software College, Northeastern University, Shenyang 110819, China.

摘要

PET和CT融合图像结合了解剖和功能信息,具有重要的临床意义。本文提出了一种基于自适应脉冲耦合神经网络(PCNN)的非二次采样轮廓波变换(NSCT)融合框架,来融合全身PET和CT图像。首先,选取每个像素的梯度平均值作为PCNN模型的链接强度,实现自适应。最后,要改善融合性能,分别提取了新颖的修正拉普拉斯算子(NSML)和边缘能量(EOE)作为PCNN模型的低通和高通子带的外部输入。最后,采用最大区域能量的规则作为融合规则,在低通和高通波段中采用不同的能量模板。对全身PET和CT数据(每种模式包含239个切片)的实验结果表明,以上所提出的框架在七个常用的融合性能度量方面优于其他六种方法。

Abstact

The PET and CT fusion images, combining the anatomical and functional information, have important clinical meaning. This paper proposes a novel fusion framework based on adaptive pulse-coupled neural networks (PCNNs) in nonsubsampled contourlet transform (NSCT) domain for fusing whole-body PET and CT images. Firstly, the gradient average of each pixel is chosen as the linking strength of PCNN model to implement self-adaptability. Secondly, to improve the fusion performance, the novel sum-modified Laplacian (NSML) and energy of edge (EOE) are extracted as the external inputs of the PCNN models for low- and high-pass subbands, respectively. Lastly, the rule of max region energy is adopted as the fusion rule and different energy templates are employed in the low- and high-pass subbands. The experimental results on whole-body PET and CT data (239 slices contained by each modality) show that the proposed framework outperforms the other six methods in terms of the seven commonly used fusion performance metrics.

阅读原文:PMID: 28473868  PMCID: PMC5394910  DOI: 10.1155/2017/8407019


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