Xu Wanni, Fu You-Lei, Xu Huasen, Wong Kelvin K L
Xiamen Academy of Arts and Design, Fuzhou University, Xiamen 361024, China; Department of Computer Information Engineering, Nanchang Institute of Technology, Nanchang 330044, China.
Department of Computer Information Engineering, Nanchang Institute of Technology, Nanchang 330044, China; Fine Art and Design College, Quanzhou Normal University, Quanzhou 362000, China.
Comput Methods Programs Biomed. 2023 Feb;229:107304. doi: 10.1016/j.cmpb.2022.107304. Epub 2022 Dec 9.
The traditional ICM is widely used in applications, such as image edge detection and image segmentation. However, several model parameters must be set, which tend to lead to reduced accuracy and increased cost. As medical images have more complex edges, contours and details, more suitable combinatorial algorithms are needed to handle the pathological diagnosis of multiple cerebral infarcts and acute strokes, resulting in the findings being more applicable, as well as having good clinical value.
To better solve the medical image fusion and diagnosis problems, this paper introduces the image fusion algorithm based on the combination of NSCT and improved ICM and proposes low-frequency, sub-band fusion rules and high-frequency sub-band fusion rules. The above method is applied to the fusion of CT/MRI images, subsequently, three other fusion algorithms, including NSCT-SF-PCNN, NSCT-SR-PCNN and Adaptive-PCNN are compared, and the simulation results of image fusion are analyzed and validated.
According to the experimental findings, the suggested algorithm performs better than other fusion algorithms in terms of five objective evaluation metrics or subjective evaluation. The NSCT transform and the improved ICM were combined, and the outcomes were evaluated against those of other fusion algorithms. The CT/MRI medical images of healthy brain tissue, numerous cerebral infarcts and acute strokes were combined using this technique.
Medical image fusion using Adaptive-PCNN produces satisfactory results, not only in relation to improved image clarity but also in terms of outstanding edge information, high contrast and brightness.
传统的迭代条件模式（ICM）在图像边缘检测和图像分割等应用中被广泛使用。然而，必须设置几个模型参数，这往往会导致准确性降低和成本增加。由于医学图像具有更复杂的边缘、轮廓和细节，因此需要更合适的组合算法来处理多发性脑梗死和急性中风的病理诊断，从而使结果更具适用性，并具有良好的临床价值。
为了更好地解决医学图像融合与诊断问题，本文介绍了基于非下采样Contourlet变换（NSCT）和改进的ICM相结合的图像融合算法，并提出了低频、子带融合规则和高频子带融合规则。将上述方法应用于CT/MRI图像融合，随后，比较了其他三种融合算法，包括NSCT-SF-PCNN、NSCT-SR-PCNN和自适应PCNN，并对图像融合的仿真结果进行了分析和验证。
根据实验结果，所提出的算法在五个客观评价指标或主观评价方面均优于其他融合算法。将NSCT变换与改进的ICM相结合，并将结果与其他融合算法的结果进行了比较。使用该技术对健康脑组织、多发性脑梗死和急性中风的CT/MRI医学图像进行了融合。
使用自适应PCNN进行医学图像融合产生了令人满意的结果，不仅在提高图像清晰度方面，而且在突出的边缘信息、高对比度和亮度方面。
