School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India.
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632 014 Tamil Nadu, India.
Behav Neurol. 2022 Apr 14;2022:6878783. doi: 10.1155/2022/6878783. eCollection 2022.
Multimodal medical image fusion is a current technique applied in the applications related to medical field to combine images from the same modality or different modalities to improve the visual content of the image to perform further operations like image segmentation. Biomedical research and medical image analysis highly demand medical image fusion to perform higher level of medical analysis. Multimodal medical fusion assists medical practitioners to visualize the internal organs and tissues. Multimodal medical fusion of brain image helps to medical practitioners to simultaneously visualize hard portion like skull and soft portion like tissue. Brain tumor segmentation can be accurately performed by utilizing the image obtained after multimodal medical image fusion. The area of the tumor can be accurately located with the information obtained from both Positron Emission Tomography and Magnetic Resonance Image in a single fused image. This approach increases the accuracy in diagnosing the tumor and reduces the time consumed in diagnosing and locating the tumor. The functional information of the brain is available in the Positron Emission Tomography while the anatomy of the brain tissue is available in the Magnetic Resonance Image. Thus, the spatial characteristics and functional information can be obtained from a single image using a robust multimodal medical image fusion model. The proposed approach uses a generative adversarial network to fuse Positron Emission Tomography and Magnetic Resonance Image into a single image. The results obtained from the proposed approach can be used for further medical analysis to locate the tumor and plan for further surgical procedures. The performance of the GAN based model is evaluated using two metrics, namely, structural similarity index and mutual information. The proposed approach achieved a structural similarity index of 0.8551 and a mutual information of 2.8059.
多模态医学图像融合是一种当前应用于医学领域相关应用的技术，用于将来自同一模态或不同模态的图像进行组合，以提高图像的视觉内容，从而执行进一步的操作，如图像分割。生物医学研究和医学图像分析高度需要医学图像融合来执行更高层次的医学分析。多模态医学融合有助于医疗从业者可视化内部器官和组织。脑图像的多模态医学融合有助于医疗从业者同时可视化硬组织如颅骨和软组织如组织。通过利用多模态医学图像融合后获得的图像，可以准确地进行脑肿瘤分割。通过在单个融合图像中利用正电子发射断层扫描和磁共振图像获得的信息，可以准确地定位肿瘤的区域。这种方法提高了肿瘤诊断的准确性，减少了诊断和定位肿瘤所需的时间。正电子发射断层扫描提供大脑的功能信息，磁共振图像提供大脑组织的解剖结构。因此，使用稳健的多模态医学图像融合模型可以从单个图像中获取空间特征和功能信息。所提出的方法使用生成对抗网络将正电子发射断层扫描和磁共振图像融合成单个图像。所提出的方法的结果可用于进一步的医学分析，以定位肿瘤并计划进一步的手术程序。基于 GAN 的模型的性能使用两个指标进行评估，即结构相似性指数和互信息。所提出的方法实现了 0.8551 的结构相似性指数和 2.8059 的互信息。
