Image Stitching for Chest Digital Radiography Using the SIFT and SURF Feature Extraction by RANSAC Algorithm

A fast algorithm for material image sequential stitching

Purpose: To assess the impact of a different distortion correction (DC) method and patient geometry (sagittal balance) on the quality of spinal cord tractography rendering according to different tractography approaches.

Methods: Forty-four adults free of spinal cord diseases underwent cervical diffusion-weighted imaging. The phase-encoding direction was head→foot. Sequence with opposed polarities (foot→head) was acquired to perform DC. Eddy-current, motion effects, and susceptibility artifact correction methods were used for DC, and two deterministic and one probabilistic tractography approaches were evaluated using MRtrix and DSI Studio tractography software. Fiber length and number of fibers were extracted to evaluate the quality of the tractography rendering. For each subject, cervical lordosis was measured to assess patient geometry. The angle between the main direction of the spinal cord and the orientation of the acquisition box were computed at each spine level to assess acquisition geometry and define an angle threshold for which a tractography of good quality is no longer possible.

Results: There was a significant improvement in tractography quality after performing DC with susceptibility artifact correction using a deterministic approach based on tensor. Before DC, the angle threshold was defined at C6 (15.2°) compared with C7 (21.9°) after corrections, demonstrating the importance of spinal cord angulation for DC.

Conclusion: The impact of DC on tractography quality is greatly impacted by acquisition geometry. To obtain a good-quality tractography, we propose as a future perspective to adapt the acquisition geometry to that of the patient by automatically adjusting the acquisition box.

Effect of distortion corrections on the tractography quality in spinal cord diffusion-weighted imaging.

Interventional endovascular treatment has become the first line of management in the treatment of peripheral artery disease (PAD). However, contrast and radiation exposure continue to limit the feasibility of these procedures. This paper presents a novel hybrid image fusion system for endovascular intervention of PAD. We present two different roadmapping methods from intra- and pre-interventional imaging that can be used either simultaneously or independently, constituting the navigation system. The navigation system is decomposed into several steps that can be entirely integrated within the procedure workflow without modifying it to benefit from the roadmapping. First, a 2D panorama of the entire peripheral artery system is automatically created based on a sequence of stepping fluoroscopic images acquired during the intra-interventional diagnosis phase. During the interventional phase, the live image can be synchronized on the panorama to form the basis of the image fusion system. Two types of augmented information are then integrated. First, an angiography panorama is proposed to avoid contrast media re-injection. Information exploiting the pre-interventional computed tomography angiography (CTA) is also brought to the surgeon by means of semiautomatic 3D/2D registration on the 2D panorama. Each step of the workflow was independently validated. Experiments for both the 2D panorama creation and the synchronization processes showed very accurate results (errors of 1.24 and $$2.6 \pm 1.4$$
  mm, respectively), similarly to the registration on the 3D CTA (errors of $$1.5 \pm 0.7$$
  mm), with minimal user interaction and very low computation time. First results of an on-going clinical study highlighted its major clinical added value on intraoperative parameters. No image fusion system has been proposed yet for endovascular procedures of PAD in lower extremities. More globally, such a navigation system, combining image fusion from different 2D and 3D image sources, is novel in the field of endovascular procedures.

/pdf/a-hybrid-image-fusion-system-for-endovascular-interventions-kljo7tebs1.pdf

A hybrid image fusion system for endovascular interventions of peripheral artery disease

Under the background of telemedicine, a new registration and mosaic algorithm for medical images is proposed in this paper to solve the problems of electronic noise, uneven illumination and ray scattering in the real-time medical process. The improved Retinex algorithm by trilateral filter and homomorphic filtering algorithm can enhance the image effectively in preprocessing. The improved phase correlation algorithm based on log polar transformation was used to calculate parameters, such as rotation, scaling and translation. Then, the SUSAN corner matching points were extracted in overlapping positions, the improved KD tree was used for enhancing matching efficiency. Later, matching points were purified by the improved RANSAC algorithm. Finally, images were processed by Laplacian pyramid decomposition algorithm to make the image joint seemed smooth and natural. The results of experiments and evaluation criteria confirm that the new method has high robustness in the process of medical image registration and stitching in the network.

An improved image registration and fusion algorithm

Improvement of automated image stitching system for DR X-ray images

In complex networks, link prediction can forecast missing links and identify spurious interactions has wide applications in the real world. Although the high-order structure plays a vital role in network evolution, its effect on link prediction is not always taken into consideration in traditional prediction algorithms. In this paper, we come up with a novel link prediction approach based on Mutual information of the High-Order Clustering structure (MHOC). The MHOC approach integrates the effects of multiple higher-order structures of nodes and quantifies the different contributions of common neighbors with the aid of the diverse high-order clustering coefficients of nodes based on information entropy for predicting missing links. Experimental results demonstrate that in the real world network, the high-order clustering patterns of nodes can improve link prediction accuracy significantly. 

Link prediction based on the mutual information with high-order clustering structure of nodes in complex networks

Emotion affects human being's health to a great extent and it attracts lots of attention recently. Objective measurements are necessary for identifying various emotion states. As a powerful technique on high temporal resolution, Electroencephalogram (EEG) provides an effective way to quantify emotion impersonally, especially uncovering the dynamic characteristics. The implementation of time window length is one popular method for extracting dynamic interactions in the brain network. In this paper, the effect of time window length is revealed on dynamic brain network investigation. A quantitative pipeline is proposed based on brain network extraction and community mining in this paper. The EEG data of healthy subjects are recorded under sadness, happiness and neutral emotions that induced through video stimulation. Since beta (13-30Hz) band EEG signals are believed to play important role in cognitive processing, this paper focuses on the beta band EEG signals. Phase locking value (PLV) was adopted to calculate the functional interactions among brain areas. A nonoverlapping time window was taken in to produce dynamic connections. As a comparison, the dynamic brain networks were extracted by the length of 2s and 5S sliding time windows respectively. Later, the Louvain algorithm was used to detect the communities. As the member of community changes over time, the stationarity is quantified to measure the evolution of the varying communities in the brain network. The preliminary results illustrated that significant difference exists among communities from the beta band EEG networks that derived from 2s and 5s time windows separately. It is suggested that the choice of sliding time window will affect the stationarity quantification of the dynamic evolution of the brain network under various emotional conditions. It is necessary to pay attention to the choice of time length window when uncovering dynamic brain network changes in the future.

The effect of time window length on dynamic brain network analysis under various emotional conditions

In cognitive science such as emotion processing, the subjects are required to repeat the experiment many times. However, less attention has been paid to the quantification evaluation of consistent variation. In this study, a pipeline is proposed to quantify and detect the significant difference that existed in repeated experiments. At first, a public electroencephalography (EEG) dataset is enrolled where the emotion tasks repeat three times for per subject. Then $\alpha\beta\delta \gamma \delta$ band EEG signals are extracted from Butterworth bandpass filter. Later, differential entropy and representational similarity analysis are combined to detect variation from inter-subject similarity analysis. Inter-subject similarity matrix is adopted to quantify the difference in repeated experiments. Our preliminary results demonstrate that significant differences are detected in certain electrodes during the repeated experiments.

Quantified variation in repeated experiments according to representational similarity analysis

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Papers

Improvement of automated image stitching system for DR X-ray images

Link prediction based on the mutual information with high-order clustering structure of nodes in complex networks

The effect of time window length on dynamic brain network analysis under various emotional conditions

Quantified variation in repeated experiments according to representational similarity analysis