Registration Success Rate Research Articles

Intelligent automatic registration: is it feasible and efficient for application of ultrasound fusion imaging in liver?

To evaluate the feasibility and efficiency between the two intelligent auto-registrations (based on hepatic vessels or based on liver surface) and manual registration for US-CT/MR fusion imaging of liver tumours. From May 2017 to December 2017, 30 patients with 30 liver tumours were enrolled in this prospectively study. Two intelligent auto-registrations (based on hepatic vessels or based on liver surface) and manual registration were randomly performed, the registration success rate and efficiency were compared. In terms of success rate, auto-registrations based on the hepatic vessels (80%) was lower than auto-registration base on liver surface and manual registration (96.67%), but with no statistical difference (P = 0.125). In comparison of the registration efficiency, the efficiency of the auto-registration based on the hepatic vessels was superior to auto-registration based on liver surface and manual registration (P < 0.05). The one-step success rate of auto-registration based on the hepatic vessels (53.33%, 16/30) was higher than that of other two registrations (P < 0.05). Stratified analysis showed that, for the lesion with display of hepatic vessels in grade 3, the success rate of auto-registration based on vessels (0%) was lower than that of auto-registration based on liver surface and manual registration (100%) (P = 0.031). Intelligent auto-registration based on hepatic vessels is a feasible and efficient registration method for US-CT/MR fusion imaging of liver tumours for the patients with clear hepatic vessels. The auto-registration based on liver surface and manual registration can be an effective supplement for cases with poor hepatic vessels display.

Accuracy and efficiency of 2D/3D single-vertebra spine navigation registration method based on dual-view feature fusion

Objective: To investigate the accuracy and efficiency of spine 2D/3D preoperative CT and intraoperative X-ray registration through a framework for spine 2D/3D single-vertebra navigation registration based on the fusion of dual-position image features. Methods: The preoperative CT and intraoperative anteroposterior (AP) and lateral (LAT) X-ray images of 140 lumbar spine patients who visited Huashan Hospital Affiliated to Fudan University from January 2020 to December 2023 were selected. In order to achieve rapid and high-precision single vertebra registration in clinical orthopedic surgery, a designed transformation parameter feature extraction module combined with a lightweight module of channel and spatial attention (CBAM) was used to accurately extract the local single vertebra image transformation information. Subsequently, the fusion regression module was used to complement the features of the anterior posterior (AP) and lateral (LAT) images to improve the accuracy of the registration parameter regression. Two 1×1 convolutions were used to reduce the parameter calculation amount, improve computational efficiency, and accelerate intraoperative registration time. Finally, the regression module outputed the final transformation parameters. Comparative experiments were conducted using traditional iterative methods (Opt-MI, Opt-NCC, Opt-C2F) and existing deep learning methods convolutional neural network (CNN) as control group. The registration accuracy (mRPD), registration time, and registration success rate were compared among the iterative methods. Results: Through experiments on real CT data, the image-guided registration accuracy of the proposed method was verified. The method achieved a registration accuracy of (0.81±0.41) mm in the mRPD metric, a rotational angle error of 0.57°±0.24°, and a translation error of (0.41±0.21) mm. Through experimental comparisons on mainstream models, the selected DenseNet alignment accuracy was significantly better than ResNet as well as VGG (both P<0.05). Compared to existing deep learning methods [mRPD: (2.97±0.99) mm, rotational angle error: 2.64°±0.54°, translation error: (2.15±0.41) mm, registration time: (0.03±0.05) seconds], the proposed method significantly improved registration accuracy (all P<0.05). The registration success rate reached 97%, with an average single registration time of only (0.04±0.02) seconds. Compared to traditional iterative methods [mRPD: (0.78±0.26) mm, rotational angle error: 0.84°±0.57°, translation error: (1.05±0.28) mm, registration time: (35.5±10.5) seconds], registration efficiency of the proposed method was significantly improved (all P<0.05). The dual-position study also compensated for the limitations in the single-view perspective, and significantly outperforms both the front and side single-view perspectives in terms of positional transformation parameter errors (both P<0.05). Conclusion: Compared to existing methods, the proposed CT and X-ray registration method significantly reduces registration time while maintaining high registration accuracy, achieving efficient and precise single vertebra registration.

Point cloud registration via sampling-based evolutionary multitasking

Point cloud registration (PCR) is an important task in both computer vision and pattern recognition. However, since the success of most existing two-stage PCR methods largely depends on the implicit assumption that the first coarse registration stage can provide an approximately optimal initial transformation for the second fine stage, these methods suffer from low success rate when the assumption is not satisfied. Evolutionary multitasking can find better solutions for multiple tasks simultaneously by transferring knowledge among multiple related tasks. Inspired by evolutionary multitasking, this study proposes a sampling-based multitask optimization method for PCR, which solves the problem of low registration success rate by sharing knowledge between two related tasks generated by a dataset. Specifically, one task is for roughly aligning the sampled point cloud, and the other is for the original unsampled point cloud. The main purpose of the first task is to continuously provide useful knowledge to the second task so that it can achieve precise alignment. Then, a new knowledge transfer mechanism guided by historical information is proposed, which can effectively adjust the search behavior of the original unsampled task. In addition, we develop a dynamic task complement strategy to complement the accuracy that may be lost by downsampling. The results on five different datasets show that the proposed method can achieve higher registration accuracy with higher success rate compared to other state-of-the-art PCR methods.

Three-Dimensional Quantitative Assessment of Pedicle Screw Accuracy in Clinical Utilization of a New Robotic System in Spine Surgery: A Multicenter Study.

The objective of this study was to evaluate the accuracy of pedicle screw placement in patients undergoing percutaneous pedicle screw fixation with robotic guidance, using a newly developed 3-dimensional quantitative measurement system. The study also aimed to assess the clinical feasibility of the robotic system in the field of spinal surgery. A total of 113 patients underwent pedicle screw insertion using the CUVIS-spine pedicle screw guide system (CUREXO Inc.). Intraoperative O-arm images were obtained, and screw insertion pathways were planned accordingly. Image registration was performed using paired-point registration and iterative closest point methods. The accuracy of the robotic-guided pedicle screw insertion was assessed using 3-dimensional offset calculation and the Gertzbein-Robbins system (GRS). A total of 448 screws were inserted in the 113 patients. The image registration success rate was 95.16%. The average error of entry offset was 2.86 mm, target offset was 2.48 mm, depth offset was 1.99 mm, and angular offset was 3.07°. According to the GRS grading system, 88.39% of the screws were classified as grade A, 9.60% as grade B, 1.56% as grade C, 0.22% as grade D, and 0.22% as grade E. Clinically acceptable screws (GRS grade A or B) accounted for 97.54% of the total, with no reported neurologic complications. Our study demonstrated that pedicle screw insertion using the novel robot-assisted navigation method is both accurate and safe. Further prospective studies are necessary to explore the potential benefits of this robot-assisted technique in comparison to conventional approaches.

Influence factors on registration of three-dimensional contrast-enhanced ultrasound fusion imaging in evaluating the ablative margin - A phantom study.

Registration of three-dimensional contrast-enhanced ultrasound fusion imaging (3DCEUS-FI) is time-consuming to obtain high success rate. To investigate the influence factors on registration success rate of 3DCEUS-FI. Water tank phantoms were made to obtain mimicked pre- and post- radiofrequency ablation three-dimensional contrast-enhanced ultrasound (3DCEUS) and CT images. Orthogonal trials were designed according to factors including size, depth, enhancement level of mimicked tumor, diameter and number of mimicked adjacent vessels. Mimicked pre- and post-RFA 3DCEUS images of 72 trials were fused to assess ablative margin (AM) by two radiologists. With CT images as standard, 3DCEUS-FI accuracy was considered as the consistency of AM evaluation. The inter-observer agreement and the influence factors on registration success rates were analyzed. The intraclass correlation coefficient (ICC) for the consistency of AM evaluation between CT and 3DCEUS-FI in x-axis, y-axis or z-axis was 0.840∼0.948 (P < 0.001). The ICC for inter-observer agreement was 0.840∼0.948 (P < 0.001). The success rates of registration within mimicked vessels with diameter of 2 mm were significantly lower than those with diameter of 3 mm and 4 mm. The mimicked AM measured by 3DCEUS-FI had high accuracy and inter-observer agreement. Diameter of the mimicked adjacent vessels was significantly related to success rate of registration.

Automatic registration of urban high-resolution remote sensing images based on characteristic spatial objects

Automatic registration of high-resolution remote sensing images (HRRSIs) has always been a severe challenge due to the local deformation caused by different shooting angles and illumination conditions. A new method of characteristic spatial objects (CSOs) extraction and matching is proposed to deal with this difficulty. Firstly, the Mask R-CNN model is utilized to extract the CSOs and their positioning points on the images automatically. Then, an encoding method is provided to encode each object with its nearest adjacent 28 objects according to the object category, relative distance, and relative direction. Furthermore, a code matching algorithm is applied to search the most similar object pairs. Finally, the object pairs need to be filtered by position matching to construct the final control points for automatic image registration. The experimental results demonstrate that the registration success rate of the proposed method reaches 88.6% within a maximum average error of 15 pixels, which is 28.6% higher than that of conventional optimization method based on local feature points. It is reasonable to believe that it has made a beneficial contribution to the automatic registration of HRRSIs more accurately and efficiently.

Deep hashing for global registration of untracked 2D laparoscopic ultrasound to CT

PurposeThe registration of Laparoscopic Ultrasound (LUS) to CT can enhance the safety of laparoscopic liver surgery by providing the surgeon with awareness on the relative positioning between critical vessels and a tumour. In an effort to provide a translatable solution for this poorly constrained problem, Content-based Image Retrieval (CBIR) based on vessel information has been suggested as a method for obtaining a global coarse registration without using tracking information. However, the performance of these frameworks is limited by the use of non-generalisable handcrafted vessel features.MethodsWe propose the use of a Deep Hashing (DH) network to directly convert vessel images from both LUS and CT into fixed size hash codes. During training, these codes are learnt from a patient-specific CT scan by supplying the network with triplets of vessel images which include both a registered and a mis-registered pair. Once hash codes have been learnt, they can be used to perform registration with CBIR methods.ResultsWe test a CBIR pipeline on 11 sequences of untracked LUS distributed across 5 clinical cases. Compared to a handcrafted feature approach, our model improves the registration success rate significantly from 48% to 61%, considering a 20 mm error as the threshold for a successful coarse registration.ConclusionsWe present the first DH framework for interventional multi-modal registration tasks. The presented approach is easily generalisable to other registration problems, does not require annotated data for training, and may promote the translation of these techniques.

Coarse registration of point clouds with low overlap rate on feature regions

In order to solve the problems of long time consuming and easy failure of the existing coarse registration algorithms based on global registration on two 3D point clouds with low overlap rate, we proposed a coarse registration algorithm based on feature regions and the Super 4-Points Congruent Sets (SUPER4PCS) algorithm. Firstly, intrinsic shape signatures (ISS) algorithm was used to extract and describe the features of the down-sampled point clouds. Secondly, the feature point clouds were divided into regions and the initial overlapped sub-regions were extracted. Thirdly, the complete overlapping regions were grown from the overlapped sub-regions and gradually recovered. Finally, the SUPER4PCS was used for registration on the complete overlapping regions. The experimental results showed that the geometric accuracy, registration success rate, and robustness of the proposed algorithm were better than that of SUPER4PCS and its improved algorithms, and the time consumption was one order of magnitude lower than that of SUPER4PCS on point clouds with low overlap rate.

ECPC-ICP: A 6D Vehicle Pose Estimation Method by Fusing the Roadside Lidar Point Cloud and Road Feature.

In the vehicle pose estimation task based on roadside Lidar in cooperative perception, the measurement distance, angle, and laser resolution directly affect the quality of the target point cloud. For incomplete and sparse point clouds, current methods are either less accurate in correspondences solved by local descriptors or not robust enough due to the reduction of effective boundary points. In response to the above weakness, this paper proposed a registration algorithm Environment Constraint Principal Component-Iterative Closest Point (ECPC-ICP), which integrated road information constraints. The road normal feature was extracted, and the principal component of the vehicle point cloud matrix under the road normal constraint was calculated as the initial pose result. Then, an accurate 6D pose was obtained through point-to-point ICP registration. According to the measurement characteristics of the roadside Lidars, this paper defined the point cloud sparseness description. The existing algorithms were tested on point cloud data with different sparseness. The simulated experimental results showed that the positioning MAE of ECPC-ICP was about 0.5% of the vehicle scale, the orientation MAE was about 0.26°, and the average registration success rate was 95.5%, which demonstrated an improvement in accuracy and robustness compared with current methods. In the real test environment, the positioning MAE was about 2.6% of the vehicle scale, and the average time cost was 53.19 ms, proving the accuracy and effectiveness of ECPC-ICP in practical applications.

Development and Characterization of a Chest CT Atlas.

A major goal of lung cancer screening is to identify individuals with particular phenotypes that are associated with high risk of cancer. Identifying relevant phenotypes is complicated by the variation in body position and body composition. In the brain, standardized coordinate systems (e.g., atlases) have enabled separate consideration of local features from gross/global structure. To date, no analogous standard atlas has been presented to enable spatial mapping and harmonization in chest computational tomography (CT). In this paper, we propose a thoracic atlas built upon a large low dose CT (LDCT) database of lung cancer screening program. The study cohort includes 466 male and 387 female subjects with no screening detected malignancy (age 46-79 years, mean 64.9 years). To provide spatial mapping, we optimize a multi-stage inter-subject non-rigid registration pipeline for the entire thoracic space. Briefly, with 50 scans of 50 randomly selected female subjects as fine tuning dataset, we search for the optimal configuration of the non-rigid registration module in a range of adjustable parameters including: registration searching radius, degree of keypoint dispersion, regularization coefficient and similarity patch size, to minimize the registration failure rate approximated by the number of samples with low Dice similarity score (DSC) for lung and body segmentation. We evaluate the optimized pipeline on a separate cohort (100 scans of 50 female and 50 male subjects) relative to two baselines with alternative non-rigid registration module: the same software with default parameters and an alternative software. We achieve a significant improvement in terms of registration success rate based on manual QA. For the entire study cohort, the optimized pipeline achieves a registration success rate of 91.7%. The application validity of the developed atlas is evaluated in terms of discriminative capability for different anatomic phenotypes, including body mass index (BMI), chronic obstructive pulmonary disease (COPD), and coronary artery calcification (CAC).

A coarse-to-fine registration method for three-dimensional MR images.

Three-dimensional (3D) multimodal magnetic resonance (MR) image registration aims to align similar things in different MR images spatially. Such a technology is useful in auxiliary disease diagnosis and surgical treatment. However, inconsistent intensity correspondence and large initial displacement contribute to the difficulty in registering multimodal MR volumes. A coarse-to-fine method is proposed in this study for pairwise 3D MR image rigid registration. Firstly, the proposed method extracts image feature points to form unregistered point sets and performs coarse registration based on point set registration to reduce the initial displacements of offset images effectively. Then, this method calculates a grey histogram based on voxels in the adaptive region of interest and further improves registration accuracy by maximizing mutual information of coarse-registered images. Some representative registration methods are compared on the basis of three MR image datasets to evaluate the performance of the proposed method. Experimental results show that the proposed method improved more in registration success rate and accuracy compared with conventional registration methods, especially when initial displacements are large.

Iterative registration for multi‐modality retinal fundus photographs using directional vessel skeleton

This paper proposes an automated registration method for multi-modality retinal fundus photographs based on the directional vessel skeleton. The main purpose is to register two retinal fundus photographs with different modalities of the same scanning region, which can provide multi-modality information for clinicians to diagnose retinal diseases or to make a treatment decision. The directional vessel skeleton of each fundus image is first detected by bias field correction and Gabor filter. The final registered fundus photographs are then obtained by the iterative affine registration between the detected directional vessel skeletons of two photographs. In this work, four kinds of fundus photographs in the macular regions of the patient with diseases, consisting of 20 optical coherence tomography fundus images, 20 colour fundus photographs, 20 fluorescein fundus angiography images and 20 indocyanine green angiography images, are utilised to quantitatively evaluate the proposed method. The root-mean-square errors show an advantageous performance in both registration success rate and accuracy.

Multi-View Point-Based Registration for Native Knee Kinematics Measurement with Feature Transfer Learning

Deep-learning methods provide a promising approach for measuring in-vivo knee joint motion from fast registration of two-dimensional (2D) to three-dimensional (3D) data with a broad range of capture. However, if there are insufficient data for training, the data-driven approach will fail. We propose a feature-based transfer-learning method to extract features from fluoroscopic images. With three subjects and fewer than 100 pairs of real fluoroscopic images, we achieved a mean registration success rate of up to 40%. The proposed method provides a promising solution, using a learning-based registration method when only a limited number of real fluoroscopic images is available.

A fast object registration method for augmented reality assembly with simultaneous determination of multiple 2D-3D correspondences

Abstract A fast and robust object registration is one of the fundamental steps of an augmented reality (AR) assembly guidance system. To manage the registration of texture-less objects from monocular images, this paper presents a three-dimensional (3D) object registration solution in the view-based framework, which incorporates a new image feature named as chain-of-lines feature (COLF). The COLF, constructed by several directed line segments with geometric constraints, enhances the robustness of the registration by establishing multiple correspondences between the two-dimensional (2D) image and 3D model simultaneously. In order to save on computational cost and improve the efficiency of the COLF-based registration, a compression algorithm based on the local breadth-first search (LBFS) strategy is also proposed; this adaptively reduces the number of keyframes according to the target model. In the experimental evaluation, the combination of COLF and LBFS-based keyframe compression shows higher registration success rate and faster speed in comparison with conventional registration methods. Moreover, an AR assembly guidance prototype system for reductors is introduced to illustrate the application of the proposed registration method.

Investigating the Accuracy of Ultrasound-Ultrasound Fusion Imaging for Evaluating the Ablation Effect via Special Phantom-Simulated Liver Tumors

The goal of this study was to investigate the accuracy of ultrasound-ultrasound (US-US) fusion imaging for evaluating the ablation effect via phantom-simulated liver tumors. Twenty special phantom models were established, ablated and divided into a complete ablation group (n = 10) and an incomplete ablation group (n = 10). US-US fusion imaging was performed to evaluate the ablation effect. Gross specimens were observed as a standard reference. In this US-US fusion imaging study, the registration success rate was 100% (20/20), and the assessment time was 3.8 ± 0.9 min. The accuracy rate of the evaluation was 100% (20/20). There was no significant difference in the residual pseudo-tumoral area between the evaluation with US-US fusion imaging and gross specimen observation (p = 0.811), and the measurement error was 1.1 ± 0.6 mm. In conclusion, the feasibility and accuracy of US-US fusion imaging when evaluating the ablation effect can be investigated with this phantom-simulated liver tumor ablation model in an ideal state.

Eliminating the Effect of Image Border with Image Periodic Decomposition for Phase Correlation Based Remote Sensing Image Registration.

In the remote sensing community, accurate image registration is the prerequisite of the subsequent application of remote sensing images. Phase correlation based image registration has drawn extensive attention due to its high accuracy and high efficiency. However, when the Discrete Fourier Transform (DFT) of an image is computed, the image is implicitly assumed to be periodic. In practical application, it is impossible to meet the periodic condition that opposite borders of an image are alike, and image always shows strong discontinuities across the frame border. The discontinuities cause a severe artifact in the Fourier Transform, namely the known cross structure composed of high energy coefficients along the axes. Here, this phenomenon was referred to as effect of image border. Even worse, the effect of image border corrupted its registration accuracy and success rate. Currently, the main solution is blurring out the border of the image by weighting window function on the reference and sensed image. However, the approach also inevitably filters out non-border information of an image. The existing understanding is that the design of window function should filter as little information as possible, which can improve the registration success rate and accuracy of methods based on phase correlation. In this paper, another approach of eliminating the effect of image border is proposed, namely decomposing the image into two images: one being the periodic image and the other the smooth image. Replacing the original image by the periodic one does not suffer from the effect on the image border when applying Fourier Transform. The smooth image is analogous to an error image, which has little information except at the border. Extensive experiments were carried out and showed that the novel algorithm of eliminating the image border can improve the success rate and accuracy of phase correlation based image registration in some certain cases. Additionally, we obtained a new understanding of the role of window function in eliminating the effect of image border, which is helpful for researchers to select the optimal method of eliminating the effect of image border to improve the registration success rate and accuracy.

The value of three-dimensional contrast-enhanced ultrasound fusion imaging in evaluating the ablation margin of radiofrequency ablation for hepatocellular carcinoma

Objective To compare three-dimensional contrast-enhanced ultrasound (3DCEUS) fusion imaging and computed tomography (CT) fusion imaging in evaluating ablation margin (AM) after radiofrequency ablation (RFA) for hepatocellular carcinoma (HCC). Methods The 3DCEUS images of 60 patients before and after RFA were collected. The AM was evaluated by the self-developed 3DCEUS fusion imaging technique. The consistency of AM evaluation was compared between 3DCEUS and CT fusion imaging. The risk factors of local tumor progression (LTP) including AM were analyzed. Results The registration success rate of 3DCEUS fusion imaging was 96.7% (58/60). Thirty-one cases were in the AM<5 mm group, and 27 cases were in the AM≥5 mm group. The consistency of AM evaluation between 3DCEUS and CT fusion imaging was good (Kappa coefficient=0.895, P<0.001). During a follow-up period ranging 4.2 to 18 months, LTP was identified in 5 tumors (8.6%, 5/58). The incidence of LTP with the AM<5 mm was higher than that with the AM≥5 mm(P=0.033). Conclusions 3DCEUS fusion imaging is feasible for AM evaluation immediately after RFA with high consistency with CT fusion imaging. AM<5 mm evaluated on 3DCEUS fusion immediately after RFA is a risk factor for LTP. Key words: Contrast-enhanced ultrasound, three-dimensional; Carcinoma, hepatocellular; Fusion imaging; Radiofrequency ablation

Three-dimensional contrast-enhanced ultrasound fusion imaging predicts local tumor progression by evaluating ablative margin of radiofrequency ablation for hepatocellular carcinoma: a preliminary report

Purpose: To investigate the feasibility of three-dimensional contrast-enhanced ultrasound (3DCEUS) fusion in evaluating ablative margin (AM) after radiofrequency ablation (RFA) for hepatocellular carcinoma (HCC) and compare with those of computed tomography (CT) fusion.Patients and methods: In total, 55 patients (47 men, 8 women; mean age, 56.0 ± 10.6 years) with 55 HCCs were included. Pre- and post-RFA 3DCEUS images were fused to assess AM immediately after RFA. The AM measured on fusion imaging was categorized into two groups: (A) AM <5 mm, and (B) AM ≥5 mm. The agreement of AM evaluation was compared between 3DCEUS and CT fusion. The influence of AMs evaluated by 3DCEUS fusion on the risk of local tumor progression (LTP) was assessed.Results: For 3DCEUS fusion, registration success rate was 96.4% (53/55). AM evaluation resulted in 28 tumors in group A and 25 tumors in group B. For CT fusion, AM evaluation resulted in 31 tumors with AM <5 mm and 23 tumors with AM ≥5 mm. AM was successful evaluated by both technique in 53 (96.4%) tumors. The agreement between 3DCEUS and CT fusion was excellent (kappa coefficient = 0.924, p < .001). LTP was identified in 5 tumors (9.4%, 5/53). LTPs all occurred in the area where unachieved 5 mm AM was evaluated by 3DCEUS fusion. Cumulative rate of LTP was significantly higher in group A than in group B (28% versus 0%; p = .033, log-rank test).Conclusions: 3DCEUS fusion enables AM evaluation for HCC immediately after RFA with comparable accuracy to CT fusion. AM <5 mm evaluated by 3DCEUS fusion has high predictive value for LTP.

Immediate evaluation and guidance of liver cancer thermal ablation by three-dimensional ultrasound/contrast-enhanced ultrasound fusion imaging

Purpose: To investigate the feasibility and value of three-dimensional ultrasound/contrast-enhanced ultrasound (3D US-CEUS) fusion imaging for the immediate evaluation of technical success and the guidance of supplementary ablation during the liver cancer thermal ablation procedure.Materials and methods: Patients diagnosed with malignant liver cancer intending to receive thermal ablation including radiofrequency ablation (RFA) or microwave ablation (MWA) were enrolled. 3D US-CEUS fusion imaging was used to immediately assess the technical success and guide supplementary ablation. Contrast-enhanced computed tomography/magnetic resonance imaging (CECT/CEMRI) was performed one month after ablation to assess the technique effectiveness of the ablation. The registration success rate, duration time of 3D US-CEUS fusion imaging, technique effectiveness rate and major complications were recorded.Results: In total, 76 patients with 95 tumours who underwent RFA or MWA and assessed by 3D US-CEUS fusion imaging were enrolled. The registration success rate of 3D US-CEUS fusion imaging was 93.7% (89/95), and the duration time was 4.0 ± 1.1 min. Thirty lesions received supplementary ablation immediately during the procedure. The technique effectiveness rate of the ablation was 98.8% (81/82). There were no major complications related to ablation.Conclusions: 3D US-CEUS fusion imaging is a feasible and valuable technique for the immediate evaluation and guidance of supplementary ablation during the liver cancer thermal ablation procedure.

An Image Registration Method Based on Wavelet Transform and Ant Colony Optimization

Image registration, as one of the basic tasks of image processing, is the process to register two images about the same objective or background which are acquired in different times, different sensors, different perspectives and different shooting conditions. In the image registration, because of the problems that the image information is complicated, they have strong correlation and incompleteness, inaccuracy and non-construction occur in different levels in the processing, to apply the method of computational intelligence information processing in the image registration can have better results than the traditional computation methods. This paper proposes an image registration method based on wavelet decomposition and ant colony optimization, which divides the process of image registration into coarse registration and refined registration through wavelet decomposition technique. In the coarse registration, the transformation parameter value of the image approximation component is acquired through ant colony optimization while the changing parameter value of the original image is obtained by the ant colony search method in the refined registration. The simulation experiment shows that this registration method has the characteristics of anti-noise, fast speed, high accuracy and high registration success rate.