Medical image fusion is widely used in various clinical procedures for the precise diagnosis of a disease. Image fusion procedures are used to assist real-time image-guided surgery. These procedures demand more accuracy and less computational complexity in modern diagnostics. Through the present work, we proposed a novel image fusion method based on stationary wavelet transform (SWT) and texture energy measures (TEMs) to address poor contrast and high-computational complexity issues of fusion outcomes. SWT extracts approximate and detail information of source images. TEMs have the capability to capture various features of the image. These are considered for fusion of approximate information. In addition, the morphological operations are used to refine the fusion process. Datasets consisting of images of seven patients suffering from neurological disorders are used in this study. Quantitative comparison of fusion results with visual information fidelity-based image fusion quality metric, ratio of spatial frequency error, edge information-based image fusion quality metric, and structural similarity index-based image fusion quality metrics proved the superiority. Also, the proposed method is superior in terms of average execution time to state-of-the-art image fusion methods. The proposed work can be extended for fusion of other imaging modalities like fusion of functional image with an anatomical image. Suitability of the fused images by the proposed method for image analysis tasks needs to be studied. 相似文献
This study describes the measurement of fields of relative displacement between the brain and the skull in vivo by tagged magnetic resonance imaging and digital image analysis. Motion of the brain relative to the skull occurs during normal activity, but if the head undergoes high accelerations, the resulting large and rapid deformation of neuronal and axonal tissue can lead to long-term disability or death. Mathematical modelling and computer simulation of acceleration-induced traumatic brain injury promise to illuminate the mechanisms of axonal and neuronal pathology, but numerical studies require knowledge of boundary conditions at the brain–skull interface, material properties and experimental data for validation. The current study provides a dense set of displacement measurements in the human brain during mild frontal skull impact constrained to the sagittal plane. Although head motion is dominated by translation, these data show that the brain rotates relative to the skull. For these mild events, characterized by linear decelerations near 1.5g (g = 9.81 m s−2) and angular accelerations of 120–140 rad s−2, relative brain–skull displacements of 2–3 mm are typical; regions of smaller displacements reflect the tethering effects of brain–skull connections. Strain fields exhibit significant areas with maximal principal strains of 5 per cent or greater. These displacement and strain fields illuminate the skull–brain boundary conditions, and can be used to validate simulations of brain biomechanics. 相似文献
In this work, a simple and efficient CAD (computer‐aided diagnostic) system is proposed for tumor detection from brain magnetic resonance imaging (MRI). Poor contrast MR images are preprocessed by using morphological operations and DSR (dynamic stochastic resonance) technique. The appropriate segmentation of MR images plays an important role in yielding the correct detection of tumor. On examination of three views of brain MRI, it was visible that the region of interest (ROI) lies in the middle and its size ranges from 240 × 240 mm2 to 280 × 280 mm2. The proposed system makes effective use of this information and identifies four blocks from the desired ROI through block‐based segmentation. Texture and shape features are extracted for each block of all MRIs in the training set. The range of these feature values defines the threshold to distinguish tumorous and nontumorous MRIs. Features of each block of an MRI view are checked against the threshold. For a particular feature, if a block is found tumorous in a view, then the other views are also checked for the presence of tumor. If corresponding blocks in all the views are found to be tumorous, then the MRI is classified as tumorous. This selective block processing technique improves computational efficiency of the system. The proposed technique is well adaptive and fast, and it is compared with well‐known existing techniques, like k‐means, fuzzy c‐means, etc. The performance analysis based on accuracy and precision parameters emphasizes the effectiveness and efficiency of the proposed work. 相似文献
This research proposes an improved hybrid fusion scheme for non-subsampled contourlet transform (NSCT) and stationary wavelet transform (SWT). Initially, the source images are decomposed into different sub-bands using NSCT. The locally weighted sum of square of the coefficients based fusion rule with consistency verification is used to fuse the detailed coefficients of NSCT. The SWT is employed to decompose approximation coefficients of NSCT into different sub-bands. The entropy of square of the coefficients and weighted sum-modified Laplacian is employed as the fusion rules with SWT. The final output is obtained using inverse NSCT. The proposed research is compared with existing fusion schemes visually and quantitatively. From the visual analysis, it is observed that the proposed scheme retained important complementary information of source images in a better way. From the quantitative comparison, it is seen that this scheme gave improved edge information, clarity, contrast, texture, and brightness in the fused image. 相似文献
Abnormal growth of cells in brain leads to the formation of tumors in brain. The earlier detection of the tumors in brain will save the life of the patients. Hence, this article proposes a computer‐aided fully automatic methodology for brain tumor detection using Co‐Active Adaptive Neuro Fuzzy Inference System (CANFIS) classifier. The internal region of the brain image is enhanced using image normalization technique and further contourlet transform is applied on the enhanced brain image for the decomposition with different scales. The grey level and heuristic features are extracted from the decomposed coefficients and these features are trained and classified using CANFIS classifier. The performance of the proposed brain tumor detection is analyzed in terms of classification accuracy, sensitivity, specificity, and segmentation accuracy. 相似文献
Surgical resection is a mainstay in the treatment of malignant brain tumors. Surgeons, however, face great challenges in distinguishing tumor margins due to their infiltrated nature. Here, a pair of gold nanoprobes that enter a brain tumor by crossing the blood–brain barrier is developed. The acidic tumor environment triggers their assembly with the concomitant activation of both magnetic resonance (MR) and surface‐enhanced resonance Raman spectroscopy (SERRS) signals. While the bulky aggregates continuously trap into the tumor interstitium, the intact nanoprobes in normal brain tissue can be transported back into the blood stream in a timely manner. Experimental results show that physiological acidity triggers nanoparticle assembly by forming 3D spherical nanoclusters with remarkable MR and SERRS signal enhancements. The nanoprobes not only preoperatively define orthotopic glioblastoma xenografts by magnetic resonance imaging (MRI) with high sensitivity and durability in vivo, but also intraoperatively guide tumor excision with the assistance of a handheld Raman scanner. Microscopy studies verify the precisely demarcated tumor margin marked by the assembled nanoprobes. Taking advantage of the nanoprobes' rapid excretion rate and the extracellular acidification as a hallmark of solid tumors, these nanoprobes are promising in improving brain‐tumor surgical outcome with high specificity, safety, and universality. 相似文献
Brain magnetic resonance imaging (MRI) is crucial for diagnosing and understanding neurological disorders, but inherent limitations hinder the visualization of fine details in brain structures. The emergence of super-resolution techniques, especially deep-learning methods, has improved imaging quality of MRI, by increasing MRI spatial resolution. At present, deep-learning algorithms mostly performed super-resolution on 2D MRI images. However, considering 3D nature of MRI, 3D models are more suitable for brain MRI super-resolution. To achieve finer brain structural details, this study proposes a 3D brain MRI super-resolution method based on diffusion model (3D-SRDM), which is a fast and easily trainable neural network for the generation of high-resolution brain MRI images. In our 3D-SRDM model, the self-attention module in U-Net is replaced with 3D spatial attention mechanism. The network structure of 3D-SRDM is optimized to reduce training parameters. Moreover, accelerated sampling from denoising diffusion implicit model is also incorporated to reduce time consumption. By these optimizations, compared with original diffusion model, the proposed model can achieve about 10- and 5-fold speed increase at 4× and 8× super-resolution of 3D brain MRI volumes, respectively, almost without affecting image quality. Thus, 3D-SRDM has potential application value in efficiently generating high-resolution 3D brain MRI images, thus facilitating the doctors' diagnosis. 相似文献
Necrosis is a form of cell death that occurs only under pathological conditions such as ischemic diseases and traumatic brain injury (TBI). Non-invasive imaging of the affected tissue is a key component of novel therapeutic interventions and measurement of treatment responses in patients. Here, we report a bimodal approach for the detection and monitoring of TBI. PEGylated poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs), encapsulating both near infrared (NIR) fluorophores and perfluorocarbons (PFCs), were targeted to necrotic cells. We used cyanine dyes such as IRDye 800CW, for which we have previously demonstrated specific targeting to intracellular proteins of cells that have lost membrane integrity. Here, we show specific in vivo detection of necrosis by optical imaging and fluorine magnetic resonance imaging (19F MRI) using newly designed PLGA NP(NIR700 + PFC)-PEG-800CW. Quantitative ex vivo optical imaging and 19F MR spectroscopy of NIR-PFC content in injured brain regions and in major organs were well correlated. Both modalities allowed the in vivo identification of necrotic brain lesions in a mouse model of TBI, with optical imaging being more sensitive than 19F MRI. Our results confirm increased blood pool residence time of PLGA NPs coated with a PEG layer and the successful targeting of TBI-damaged tissue. A single PLGA NP containing NIR-PFC enables both rapid qualitative optical monitoring of the TBI state and quantitative 3D information from deeper tissues on the extent of the lesion by MRI. These necrosis-targeting PLGA NPs can potentially be used for clinical diagnosis of brain injuries.
Many types of medical images must be fused, as single‐modality medical images can only provide limited information due to the imaging principles and the complexity of human organ structures. In this paper, a multimodal medical image fusion method that combines the advantages of nonsubsampling contourlet transform (NSCT) and fuzzy entropy is proposed to provide a basis for clinical diagnosis and improve the accuracy of target recognition and the quality of fused images. An image is initially decomposed into low‐ and high‐frequency subbands through NSCT. The corresponding fusion rules are adopted in accordance with the different characteristics of the low‐ and high‐frequency components. The membership degree of low‐frequency coefficients is calculated. The fuzzy entropy is also computed and subsequently used to guide the fusion of coefficients to preserve image details. High‐frequency components are fused by maximizing the regional energy. The final fused image is obtained by inverse transformation. Experimental results show that the proposed method achieves good fusion effect based on the subjective visual effect and objective evaluation criteria. This method can also obtain high average gradient, SD, and edge preservation and effectively retain the details of the fused image. The results of the proposed algorithm can provide effective reference for doctors to assess patient condition. 相似文献
A heuristic design method for rapid volumetric magnetic resonance imaging data acquisition trajectories is presented, using a series of second-order cone optimization subproblems. Other researchers have considered non-raster data collection trajectories and under-sampled data patterns. This work demonstrates that much higher rates of under-sampling are possible with an asymmetric set of trajectories, with very little loss in resolution, but the addition of noise-like artefacts. The proposed data collection trajectory, Durga, further minimizes collection time by incorporating short un-refocused excitation pulses, resulting in above 98% collection efficiency for balanced steady state free precession imaging. The optimization subproblems are novel, in that they incorporate all requirements, including data collection (coverage), physicality (device limits), and signal generation (zeroth- and higher- moment properties) in a single convex problem, which allows the resulting trajectories to exhibit a higher collection efficiency than any existing trajectory design. 相似文献
