More and more medical devices can capture different features of human body and form three dimensional (3D) images. In clinical applications, usually it is required to fuse multiple source images containing different and crucial information into one for the purpose of assisting medical treatment. However, traditional image fusion methods are normally designed for two dimensional (2D) images and will lead to loss of the third dimensional information if directly applied to 3D data. Therefore, a novel 3D magnetic image fusion method was proposed based on the combination of newly invented beyond wavelet transform, called 3D band limited shearlet transformand (BLST), and four groups of traditional fusion rules. The proposed method was then compared with the 2D and 3D wavelet and dual-tree complex wavelet transform fusion methods through 4 groups of human brain T2* and quantitative susceptibility mapping (QSM) images. The experiments indicated that the performance of the method based on 3D transform was generally superior to the existing methods based on 2D transform. Taking advantage of direction representation, shearlet transform could effectively improve the performance of conventional fusion method based on 3D transform. It is well concluded, therefore, that the proposed method is the best among the methods based on 2D and 3D transforms.
Medical image fusion realizes advantage integration of functional images and anatomical images. This article discusses the research progress of multi-model medical image fusion at feature level. We firstly describe the principle of medical image fusion at feature level. Then we analyze and summarize fuzzy sets, rough sets, D-S evidence theory, artificial neural network, principal component analysis and other fusion methods' applications in medical image fusion and get summery. Lastly, we in this article indicate present problems and the research direction of multi-model medical images in the future.
Both Dyna CT, a rotational faultage reconstructed technique, and digital subtraction angiography (DSA) play important roles in the diagnosis and treatment of arteriovenous malformations (AVM). Three-dimensional Dyna CT can provide the spatial information of AVM nidus, while two-dimensional DSA can provide the time information for distinguishing arteries and veins. To illustrate the location relationship of the nidus, arteries and veins at the same time, these two imaging modalities need to be fused. In this paper, a two-dimensional to three-dimensional back projection and growing method is proposed, which realizes the image fusion of two-dimensional DSA and three-dimensional Dyna CT and achieves the differentiation of arteries and veins in Dyna CT. The experimental results showed that the fusion image could present both the position information of AVM nidus and the dynamic information of the blood vessels. Therefore, the proposed method can help surgeons locate the AVM abnormality and make operation plan more accurately.
At present, most of the bone xenograft for clinical application comes from bovine. In recent years, many studies have been done on the clinical application of porcine xenograft bone. The goal of this study was to evaluate the effect of canine mandibular defects reparation with antigen-extracted porcine cancellous bone by imaging examination. Four dogs’ bilateral mandibular defects were created, with one side repaired with autologous bone (set as control group) while the other side repaired with antigen-extracted porcine cancellous bone (set as experimental group). Titanium plates and titanium screws were used for fixation. Cone beam computed tomography (CBCT), computed tomography (CT), single-photon emission computed tomography (SPECT) were undertaken at week 12 and 24 postoperatively, and SPECT and CT images were fused. The results demonstrated that the remodeling of antigen-extracted porcine cancellous bone was slower than that of autologous bone, but it can still be used as scaffold for jaw defects. The results in this study provide a new choice for materials required for clinical reparation of jaw defects.
Electrocardiogram (ECG) signal is an important basis for the diagnosis of arrhythmia and myocardial infarction. In order to further improve the classification effect of arrhythmia and myocardial infarction, an ECG classification algorithm based on Convolutional vision Transformer (CvT) and multimodal image fusion was proposed. Through Gramian summation angular field (GASF), Gramian difference angular field (GADF) and recurrence plot (RP), the one-dimensional ECG signal was converted into three different modes of two-dimensional images, and fused into a multimodal fusion image containing more features. The CvT-13 model could take into account local and global information when processing the fused image, thus effectively improving the classification performance. On the MIT-BIH arrhythmia dataset and the PTB myocardial infarction dataset, the algorithm achieved a combined accuracy of 99.9% for the classification of five arrhythmias and 99.8% for the classification of myocardial infarction. The experiments show that the high-precision computer-assisted intelligent classification method is superior and can effectively improve the diagnostic efficiency of arrhythmia as well as myocardial infarction and other cardiac diseases.