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;20:1-13. doi: 10.2174/0115734056260083230924154700.
Modern medical imaging modalities used by clinicians have many applications in the diagnosis of complicated diseases. These imaging technologies reveal the internal anatomy and physiology of the body. The fundamental idea behind medical image fusion is to increase the image's global and local contrast, enhance the visual impact, and change its format so that it is better suited for computer processing or human viewing while preventing noise magnification and accomplishing excellent real-time performance.
The top goal is to combine data from various modal images (CT/MRI and MR-T1/MR-T2) into a solitary image that, to the greatest degree possible, retains the key characteristics (prominent features) of the source images.
The clinical accuracy of medical issues is compromised because innumerable classical fusion methods struggle to conserve all the prominent features of the original images. Furthermore, complex implementation, high computation time, and more memory requirements are key problems of transform domain methods. With the purpose of solving these problems, this research suggests a fusion framework for multimodal medical images that makes use of a multi-scale edge-preserving filter and visual saliency detection. The source images are decomposed using a two-scale edge-preserving filter into base and detail layers. Base layers are combined using the addition fusion rule, while detail layers are fused using weight maps constructed using the maximum symmetric surround saliency detection algorithm.
The resultant image constructed by the presumed method has improved objective evaluation metrics than other classical methods, as well as unhindered edge contour, more global contrast, and no ringing effect or artifacts.
The methodology offers a dominant and symbiotic arsenal of clinical symptomatic, therapeutic, and biomedical research competencies that have the prospective to considerably strengthen medical practice and biological understanding.
临床医生使用的现代医学成像方式在复杂疾病的诊断中有许多应用。这些成像技术揭示了身体的内部解剖结构和生理机能。医学图像融合的基本思想是增加图像的全局和局部对比度,增强视觉效果,并改变其格式,使其更适合计算机处理或人类观察,同时防止噪声放大并实现出色的实时性能。
首要目标是将来自各种模态图像(CT/MRI 和 MR-T1/MR-T2)的数据组合成一幅单一的图像,最大程度地保留源图像的关键特征(显著特征)。
由于无数经典的融合方法难以保留原始图像的所有显著特征,因此会影响医学问题的临床准确性。此外,复杂的实现、高计算时间和更多的内存需求是变换域方法的关键问题。为了解决这些问题,本研究提出了一种用于多模态医学图像的融合框架,该框架利用多尺度边缘保持滤波器和视觉显著度检测。使用两尺度边缘保持滤波器将源图像分解为基和细节层。使用加法融合规则组合基层,而使用基于最大对称环绕显著度检测算法构建的权重图来融合细节层。
所提出的方法构建的结果图像具有比其他经典方法更好的客观评估指标,以及无阻碍的边缘轮廓、更高的全局对比度,并且没有振铃效应或伪影。
该方法提供了一种具有主导性和共生性的临床症状、治疗和生物医学研究能力的武器库,有潜力极大地加强医疗实践和生物理解。