Optimal Image Fusion Research Articles

Clustering-based image segmentation for optimal image fusion using CT and MRI images

As opposed to using many unrelated photographs to depict the same scene, image fusions combine multiple, similar images to generate a single, unified image with greater detail. Imaging sensors and the need for a wideband signal to transmit most source images limit their resolution. This study suggests new methods of fusing medical pictures from different modalities in order to increase image quality and, by extension, the accuracy with which brain tumors can be detected and identified. Improved convolutional neural network (ICNN) and region growth-based [Formula: see text]-means clustering (RKMC) are used in the suggested strategy to boost the quality of brain image fusions obtained from Computed tomography scanned image (CTSI) and magnetic resonance imaging (MRI) in this study. The first stages of this task consist of eliminating noise, segmenting images, extracting and selecting features, and fusing images. AMF (Adaptive Median Filtering) are first used to eliminate noise from MRI images and CTSI of the brain, improving the image quality. With the help of the RKMC algorithm, MRI image and CTSI scans can be segmented into their constituent pieces, which can then be seen either as grayscale images or as pictures of objects. The RKMC algorithm is able to adequately account for the possibility of tumors in white images. More useful image features can be extracted with the use of MPCA (Modified Principal Component Analysis). Afterward, features with the highest fitness values are chosen by using AFO (Adaptive Firefly Optimization). Image fusions of multimodal images are carried out using ICNN, which generates the image’s lower-, middle-, and higher-level contents. Incorporating important and relevant image characteristics from all viewpoints and perspectives improves feature training and testing. The results show that the proposed RKMC+ICNNs outperform the state-of-the-art approaches in terms of accuracy, PSNR, RMSE, and runtime.

Retraction Note to: Medical image integrated possessions assisted soft computing techniques for optimized image fusion with less noise and high contour detection

Retraction Note to: Medical image integrated possessions assisted soft computing techniques for optimized image fusion with less noise and high contour detection

Optimal wavelet-based multi-modal medical image fusion with quantitative analysis for colour images using different colour models

Optimal wavelet-based multi-modal medical image fusion with quantitative analysis for colour images using different colour models

Analysis of Mental State of Patients after Drug Addiction and Withdrawal Guided by PETCT Image Based on Optimized Image Fusion Algorithm

Blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) studies have shown that drug-dependent patients are activated in different addictive brain areas under the stimulation of relevant environmental cues, which in turn leads to craving and relapse. This study uses magnetic resonance spectroscopy to measure brain temperature to explore the brain temperature changes in different addictive brain regions of heroin and methamphetamine addicts in a short-term withdrawal state and to explore whether the quantitative index of brain temperature change can be used as a diagnostic drug Methods. The subjects were scanned by resting-state MRI spectroscopy first and then subjected to MRI spectroscopy scanning under visual stimulation. The subjects were required to watch the heroin/meth-related clue pictures carefully during visual stimulation. The measured chemical shift value of N-acetyl-aspartic acid (NAA) is substituted into the brain temperature calculation formula T = 37 + 100 to obtain the brain temperature before and after visual stimulation. In addition, the anxiety and depression states of heroin and methamphetamine-dependent patients were evaluated. Results. There was no statistically significant change in the brain temperature of the prefrontal cortex before and after visual stimulation in heroin and methamphetamine-dependent subjects; compared with the normal group, there was no change in prefrontal cortex brain temperature before and after visual stimulation in heroin and methamphetamine-dependent subjects. Statistical Significance. The changes of hippocampal temperature before and after visual stimulation in methamphetamine-dependent patients were not statistically significant; compared with the normal group, there was no statistically significant difference in the changes of hippocampal temperature before and after visual stimulation in methamphetamine-dependent patients. Conclusion. This study initially found that the visual cues related to heroin and methamphetamine were not enough to cause significant changes in the brain temperature of the prefrontal cortex.

RETRACTED ARTICLE: Medical image integrated possessions assisted soft computing techniques for optimized image fusion with less noise and high contour detection

This paper introduces an intellectual image fusion technique which is much focused on Medical Image Integrated Possessions assisted Soft Computing Techniques (MIPSCT) with fuzzy sets. This dual image fusion design uses a fuzzy mid matrix method and a smooth adjustment process which helps to eliminate impulsive noise from extremely distorted images that is included in a smart image agent when fusing image on various image processing environment. The fuzzy function used in the filter is intended to remove impulses without losing fine details and textures which are more important in image fusion modelling. Furthermore, adjust filter parameters from a set of exercise data with an image culture process based on genetic algorithm has been implemented on MIPSCT to improve contour detection. The experimental results have been analyzed based on intelligent soft computing tools in assistance with matrix laboratory to achieve better output in accordance with SDROM, AWFM, SFVQ, and DCT modelling for brain image datasets. The validation at lab scale shows promising results on Peak Signal to Noise Ratio and Absolute Mean Error (AME) parameters in accordance with conventional methods.

Iliac Artery Deformation During Abdominal EVAR has Consequences for Choice of Iliac Limbs, Optimal C-arm Angulation and Image Fusion Guidance.

Iliac Artery Deformation During Abdominal EVAR has Consequences for Choice of Iliac Limbs, Optimal C-arm Angulation and Image Fusion Guidance.

An optimal wavelet-based multi-modality medical image fusion approach based on modified central force optimization and histogram matching

This paper introduces an optimal solution for wavelet-based medical image fusion using different wavelet families and Principal Component Ana1ysis (PCA) based on the Modified Central Force Optimization (MCFO) technique. The main motivation of this work is to increase the quality of medical fused images in order to provide correct diagnosis of diseases for the objective of optimal therapy. This can be achieved by fusing medical images of different modalities using an optimization technique based on the MCFO. The MCFO technique gives the optimum gain parameters that achieve the best fused image quality. Histogram matching is applied to improve the overall values of the Peak Signal-to-Noise Ratio (PSNR), entropy, local contrast, and quality of the fused image. A comparative study is performed between the proposed algorithm, the traditional Discrete Wavelet Transform (DWT), and the PCA fusion using maximum fusion rule. The proposed algorithm is evaluated subjectively and objectively with different fusion quality metrics. Simulation results demonstrate that the proposed MCFO optimized wavelet-based fusion algorithm using Haar wavelet and histogram matching achieves a superior performance with the highest image quality and clearest image details in a very short processing time.

Multi-segmental spine image registration supporting image-guided interventions of spinal metastases

BackgroundRadiofrequency ablation was introduced recently to treat spinal metastases, which are among the most common metastases. These minimally-invasive interventions are most often image-guided by flat-panel CT scans, withholding soft tissue contrast like MR imaging. Image fusion of diagnostic MR and operative CT images could provide important and useful information during interventions. MethodDiagnostic MR and interventional flat-panel CT scans of 19 patients, who underwent radiofrequency ablations of spinal metastases were obtained. Our presented approach piecewise rigidly registers single vertebrae using normalized gradient fields and embeds them within a fused image. Registration accuracy was determined via Euclidean distances between corresponding landmark pairs of ground truth data. ResultsOur method resulted in an average registration error of 2.35mm. An optimal image fusion performed by landmark registrations achieved an average registration error of 1.70mm. Additionally, intra- and inter-reader variability was determined, resulting in mean distances of corresponding landmark pairs of 1.05mm (MRI) and 1.03mm (flat-panel CT) for the intra-reader variability and 1.36mm and 1.28mm for the inter-reader variability, respectively. ConclusionsOur multi-segmental approach with normalized gradient fields as image similarity measure can handle spine deformations due to patient positioning and avoid time-consuming manually performed registration. Thus, our method can provide practical and applicable intervention support without significantly delaying the clinical workflow or additional workload.

A social spider optimized image fusion approach for contrast enhancement and brightness preservation

Image enhancement means to process the image in order to obtain a visually pleasing effect on image with more perception details and less noise output. In this work, a social spider optimization based algorithm is used to produce two enhanced images, one having high sharpness or contrast with maximum entropy and the other having high peak signal-to-noise ratio (PSNR) sharp image. The two enhanced images are fused to obtain an output image which has an optimal trade-off between sharpness and noise. The proposed algorithms are applied to lathe tool images as well as to some other standard images to verify their effectiveness. The results are compared with conventional image enhancement techniques such as Histogram Equalization (HE), Linear Contrast Stretching (LCS), and Standard Particle Swarm Optimization (PSO) algorithm.

Optimal spectral image fusion for detection of shoreline targets

Optimal spectral image fusion for detection of shoreline targets

Image Fusion and Evaluation of Geological Disaster Based on Remote Sensing

The investigation of geological disaster in our article locates in southern Australia, which is characterized by wide range, high relief, inaccessibility and other unfavorable factors. Multi-spectral ETM+ and SPOT 5 pan images were selected as the remote sensing data source, and Brovey transform (BT), intensity-hue-saturation (IHS), principal component analysis (PCA), high-pass filtering (HPF) and modified Gram-Schmidt (MGS) methods were used for image fusion. A comparison has been conducted between the resultant fusion images to assess the image quality both in subjective and objective evaluation. The results show that, the MGS method is the optimal image fusion method for geological disaster interpretation, and can provide abundant textural and spectral information in interpreting such geological disasters as landslide, rock fall and debris flow.

A novel framework of tissue membrane systems for image fusion.

This paper proposes a tissue membrane system-based framework to deal with the optimal image fusion problem. A spatial domain fusion algorithm is given, and a tissue membrane system of multiple cells is used as its computing framework. Based on the multicellular structure and inherent communication mechanism of the tissue membrane system, an improved velocity-position model is developed. The performance of the fusion framework is studied with comparison of several traditional fusion methods as well as genetic algorithm (GA)-based and differential evolution (DE)-based spatial domain fusion methods. Experimental results show that the proposed fusion framework is superior or comparable to the other methods and can be efficiently used for image fusion.

Artificial Bee Colony approach to information granulation-based fuzzy radial basis function neural networks for image fusion

This paper mainly proposed a novel method of Artificial Bee Colony (ABC) optimized fuzzy radial basis function neural networks with information granulation (IG-FRBFNNs) for solving the image fusion problem. Image fusion is the process of combining relevant information from two or more images into a single image. The fuzzy RBF neural networks exploit the Fuzzy C-Means (FCM) clustering to form the premise part of the rules. As the consequent part of the model (being the local model representing input output relation in the corresponding sub-space), four types of polynomials are considered, with the ordinary least square (OLS) learning being exploited to estimate the values of the coefficients of the polynomial. Since the performance of the IG-FRBFNN model is directly affected by the parameters such as the fuzzification coefficient used in the FCM, the position of their centers and the values of the widths, ABC algorithm is exploited to carry out the structural and parametric optimization of the model respectively while the optimization is of multi-objective character as it is aimed at the simultaneous minimization of complexity and maximization of accuracy. Subsequently, the proposed approach can dynamically obtain optimal image fusion weights based on regional features, so as to optimize performance of image fusion. Series of experimental results are presented to verify the feasibility and effectiveness of the proposed approach.

Optimal image-fusion method based on nonsubsampled contourlet transform

The optimization of image fusion is researched. Based on the properties of nonsubsampled contourlet transform (NSCT), shift invariance, multiscale and multidirectional expansion, the fusion parameters of the multiscale decompostion scheme is optimized. In order to meet the requirement of feedback optimization, a new image fusion quality metric of image quality index normalized edge association (IQI-NEA) is built. A polynomial model is adopted to establish the relationship between the IQI_NEA metric and several decomposition levels. The optimal fusion includes four steps. First, the source images are decomposed in NSCT domain for several given levels. Second, principal component analysis is adopted to fuse the low frequency coefficients and the maximum fusion rule is utilized to fuse the high frequency coefficients to obtain the fused coefficients and the fused result is reconstructed from the obtained fused coefficients. Third, calculate the fusion quality metric IQI_NEA for the source images and fused images. Finally, the optimal fused image and optimal level are obtained through extremum properties of polynomials function. The visual and statistical results show that the proposed method has optimized the fusion performance compared to the existing fusion schemes, in terms of the visual effects and quantitative fusion evaluation indexes.

Assessing Optimal Image Fusion Methods for Very High Spatial Resolution Satellite Images to Support Coastal Monitoring

This study examines best image fusion approaches for generating pansharpened very high resolution (VHR) multispectral images to be utilized for monitoring coastal barrier island development. Selected fusion techniques assessed in this research come from the three categories of spectral substitution (e.g., Brovey transform and multiplicative merging), arithmetic merging (e.g., modified intensity-hue-saturation and principal component analysis), and spatial domain (e.g., high-pass filter, and subtractive resolution merge). The image fusion methods selected for this study were capable of producing pansharpened VHR images with more than three bands. Comparisons of fusion techniques were applied to images from three satellite sensors: United States commercial satellites IKONOS and QuickBird, and the Korean KOMPSAT II. Pansharpened VHR multispectral images were assessed by spectral and spatial quality measurements. Results satisfying both spectral and spatial quality revealed optimum pansharpened techniques necessary for regular coastal mapping of barrier islands. These techniques may also be used to assess the quality of recently available VHR imagery acquired by numerous international, government, and commercial VHR satellite programs.

An optimized strategy for real-time hemorrhage monitoring with electrical impedance tomography

Delayed detection of an internal hemorrhage may result in serious disabilities and possibly death for a patient. Currently, there are no portable medical imaging instruments that are suitable for long-term monitoring of patients at risk of internal hemorrhage. Electrical impedance tomography (EIT) has the potential to monitor patients continuously as a novel functional image modality and instantly detect the occurrence of an internal hemorrhage. However, the low spatial resolution and high sensitivity to noise of this technique have limited its application in clinics. In addition, due to the circular boundary display mode used in current EIT images, it is difficult for clinicians to identify precisely which organ is bleeding using this technique. The aim of this study was to propose an optimized strategy for EIT reconstruction to promote the use of EIT for clinical studies, which mainly includes the use of anatomically accurate boundary shapes, rapid selection of optimal regularization parameters and image fusion of EIT and computed tomography images. The method was evaluated on retroperitoneal and intraperitoneal bleeding piglet data. Both traditional backprojection images and optimized images among different boundary shapes were reconstructed and compared. The experimental results demonstrated that EIT images with precise anatomical information can be reconstructed in which the image resolution and resistance to noise can be improved effectively.

The effective application of segmental image fusion in spinal radiosurgery for improved targeting of spinal tumours

As a result of experiences of failed image fusion, an improved protocol for effective CT and MRI image fusion was developed. Image fusion is a critical part of image-guided stereotactic radiosurgery (IG-SRS) and greatly influences the accurate measurement of gross tumour volume (GTV) and optimal dosimetry. Avoidance of any positional discrepancy is vital for optimal image fusion and results in improved targeting, which improves clinical results. This paper describes a protocol for effective image fusion and how it impacted on the clinical outcome of stereotactic radiosurgery for spinal tumours. Fused MRI/CT images from 20 patients were examined and compared. A protocol for fusing images from thin slice MR images and CTs was developed for improved identification and measurement of tumour volume. Differences in individual GTV values both before and after image fusion were evaluated. The effectiveness of tumour targeting was also assessed by comparing discrepancies in individual and overall GTV values. Differences in mean GTVs using either CT or MRI alone compared with the mean found through combined CT/MR image fusion showed a difference of 30.5 +/- 4.8% and 14.5 +/- 3.3% respectively. Additionally, the median GTV values from CT- and MR-based imaging were 11.64 +/- 7.8 cm(3) and 11.72 +/- 6.6 cm(3) vs 14.06 +/- 8.0 cm(3). Median GTV from CT-MR fusion was 14.06 +/- 8.0 cm(3). Improved information provided by the fused images enabled us to prescribe more effective dosages, as the fused images gave more accurate information about tumour se due to better delineation of tumour perimeters. This protocol provides improved visualisation of spinal tumours and enables better treatment planning. Segmented image fusion was shown to provide significant advantages for planning stereotactic radiosurgery. Fused images provided more precise and accurate data and allowed better targeting of tumours, with improved tumour coverage that resulted in better clinical outcomes.

Effects of spatial resolution ratio in image fusion

In image fusion, the spatial resolution ratio can be defined as the ratio between the spatial resolution of the high‐resolution panchromatic image and that of the low‐resolution multispectral image. This paper attempts to assess the effects of the spatial resolution ratio of the input images on the quality of the fused image. Experimental results indicate that a spatial resolution ratio of 1 : 10 or higher is desired for optimal multisensor image fusion provided the input panchromatic image is not downsampled to a coarser resolution. Due to the synthetic pixels generated from resampling, the quality of the fused image decreases as the spatial resolution ratio decreases (e.g. from 1 : 10 to 1 : 30). However, even with a spatial resolution ratio as small as 1 : 30, the quality of the fused image is still better than the original multispectral image alone for feature interpretation. In cases where the spatial resolution ratio is too small (e.g. 1 : 30), to obtain better spectral integrity of the fused image, one may downsample the input high‐resolution panchromatic image to a slightly lower resolution before fusing it with the multispectral image.

Utilidad de la TC diagnóstica inspiratoria tras la realización del estudio PET/TC en la detección de nódulos pulmonares

We present the case of a 57-year old woman diagnosed of papillary thyroid carcinoma and treated with thyroidectomy followed by radioiodine (I-131) on two occasions. Follow-up radioiodine scan showed disease in right cervical region, confirmed by fine needle aspiration (FNA) and treated with lymphadenectomy. Due to thyroglobulin elevation, I-131 scan negative and inconclusive cervical ultrasonography/CT scan, we conducted a CT/PET study that confirmed cervical disease. An additional CT scan that was performed on maximum-inspiration showed four micro-nodules, one of which was not detected by the CT scan on shallow breathing (CT/PET). Post-treatment (I-131) scan confirmed uptake in these localizations. Good fusion between PET and CT images that avoids the errors of attenuation correction, especially in the lung bases, is necessary for correct image interpretation of the CT/PET study. Shallow breathing is necessary in order to obtain optimal image fusion with the CT/PET study, although this is not the best to evaluate pulmonary parenchyma in which an additional inspiratory CT scan improves detection of the pulmonary nodules.

Indikationsstellung und Planung der Strahlentherapie mittels PET / CT

The decision for radiation and planning of radiotherapy can only be defined in view of the interdisciplinary treatment concept. Therefore a correct staging is fundamental. In contrast to chemotherapy, in radiotherapy, used as a local therapy, an exact identification of the infiltrating tumor and the nodal manifestations is necessary. A correct definition of the M-status is required for the indication of radiotherapy as well as the interdisciplinary treatment concept. Imaging is essential for the definition of the treatment concept and the planning process of radiotherapy. Different morphological and functional imaging methods are available. In radiation oncology the advantages of PET can be most favorably used with PET / CT hybrid scanners because the obligatory CT-planning matrix of radiotherapy allows an anatomically optimized image fusion of PET signals. Aim of this study is the evaluation of combined anatomical and metabolic imaging in the process of staging and decision for radiation oncology.