Kaur Harmanpreet, Vig Renu, Kumar Naresh, Sharma Apoorav, Dogra Ayush, Goyal Bhawna
Department of Electronics and Communication Engineering, UIET, Panjab University, Chandigarh 160014, India.
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
Curr Med Imaging. 2024 Jan 26. doi: 10.2174/0115734056269626231201042100.
A clinical medical image provides vital information about a person's health and bodily condition. Typically, doctors monitor and examine several types of medical images individually to gather supplementary information for illness diagnosis and treatment. As it is arduous to analyze and diagnose from a single image, multi-modality images have been shown to enhance the precision of diagnosis and evaluation of medical conditions.
Several conventional image fusion techniques strengthen the consistency of the information by combining varied image observations; nevertheless, the drawback of these techniques in retaining all crucial elements of the original images can have a negative impact on the accuracy of clinical diagnoses. This research develops an improved image fusion technique based on fine-grained saliency and an anisotropic diffusion filter to preserve structural and detailed information of the individual image.
In contrast to prior efforts, the saliency method is not executed using a pyramidal decomposition, but rather an integral image on the original scale is used to obtain features of superior quality. Furthermore, an anisotropic diffusion filter is utilized for the decomposition of the original source images into a base layer and a detail layer. The proposed algorithm's performance is then contrasted to those of cutting-edge image fusion algorithms.
The proposed approach cannot only cope with the fusion of medical images well, both subjectively and objectively, according to the results obtained, but also has high computational efficiency.
Furthermore, it provides a roadmap for the direction of future research.
临床医疗图像提供了有关个人健康和身体状况的重要信息。通常，医生会分别监测和检查几种类型的医学图像，以收集疾病诊断和治疗的补充信息。由于从单一图像进行分析和诊断很困难，多模态图像已被证明可以提高医疗状况诊断和评估的准确性。
几种传统的图像融合技术通过组合不同的图像观测来增强信息的一致性；然而，这些技术在保留原始图像所有关键元素方面的缺点可能会对临床诊断的准确性产生负面影响。本研究开发了一种基于细粒度显著性和各向异性扩散滤波器的改进图像融合技术，以保留单个图像的结构和详细信息。
与之前的努力不同，显著性方法不是使用金字塔分解来执行，而是在原始尺度上使用积分图像来获得更高质量的特征。此外，利用各向异性扩散滤波器将原始源图像分解为一个基础层和一个细节层。然后将所提出算法的性能与前沿图像融合算法的性能进行对比。
根据获得的结果，所提出的方法不仅在主观和客观上都能很好地处理医学图像的融合，而且具有很高的计算效率。
此外，它为未来的研究方向提供了路线图。
