Aiming at the early problems of video-based smoke detection in fire video, this paper proposes a method to extract smoke suspected regions by combining two steps segmentation and motion characteristics. Early smoldering smoke can be seen as gray or gray-white regions. In the first stage, regions of interests (ROIs) with smoke are obtained by using two step segmentation methods. Then, suspected smoke regions are detected by combining the two step segmentation and motion detection. Finally, morphological processing is used for smoke regions extracting. The Otsu algorithm is used as segmentation method and the ViBe algorithm is used to detect the motion of smoke. The proposed method was tested on 6 test videos with smoke. The experimental results show the effectiveness of our proposed method over visual observation.
A signal conditioning units for vibration measurement in HUMS is proposed in the paper. Due to the frequency of vibrations caused by components in helicopter are different, two steps amplifier and programmable anti-aliasing filter are designed to meet the measurement of different types of helicopter. Vibration signals are converted into measurable electrical signals combing with ICP driver firstly. Then pre-amplifier and programmable gain amplifier is applied to magnify the weak electrical signals. In addition, programmable anti-aliasing filter is utilized to filter the interference of noise. The units were tested using function signal generator and oscilloscope. The experimental results have demonstrated the effectiveness of our proposed method in quantitatively and qualitatively. The method presented in this paper can meet the measurement requirement for different types of helicopter.
Image acquired during free breathing using contrast enhanced ultrasound (CEUS) hepatic perfusion imaging exhibits a periodic motion pattern. It needs to be compensated for if a further accurate quantification of the hepatic perfusion analysis is to be executed. A respiratory motion compensation strategy for CEUS imaging by using image clustering is proposed in this work. The proposed strategy separated the dual mode image to tissue image and contrast image firstly. Then, the image subsequences based on the tissue image are determined by using sparse subspace clustering (SSC) method. Finally, the motion compensated contrast images are acquired by using the position mapping. The strategy was tested on ten CEUS hepatic perfusion image sequences. Quantitative and visual comparisons demonstrate that the proposed strategy can compensate the misalignment of ultrasound hepatic perfusion image sequence caused by respiratory motion in free-breathing.
Respiratory motion is a challenging factor for image acquisition, image-guided procedures and perfusion quantification using contrast-enhanced ultrasound in the abdominal and thoracic region. In order to reduce the influence of respiratory motion, respiratory correction methods were investigated. In this paper we propose a novel, cluster-based respiratory correction method. In the proposed method, we assign the image frames of the corresponding respiratory phase using spectral clustering firstly. And then, we achieve the images correction automatically by finding a cluster in which points are close to each other. Unlike the traditional gating method, we don’t need to estimate the breathing cycle accurate. It is because images are similar at the corresponding respiratory phase, and they are close in high-dimensional space. The proposed method is tested on simulation image sequence and real ultrasound image sequence. The experimental results show the effectiveness of our proposed method in quantitative and qualitative.
Images acquired during free breathing using contrast enhanced ultrasound hepatic perfusion imaging exhibits a periodic motion pattern. It needs to be compensated for if a further accurate quantification of the hepatic perfusion analysis is to be executed. To reduce the impact of respiratory motion, image-based breathing gating algorithm was used to compensate the respiratory motion in contrast enhanced ultrasound. The algorithm contains three steps of which respiratory kinetics extracted, image subsequences determined and image subsequences registered. The basic performance of the algorithm was to extract the respiratory kinetics of the ultrasound hepatic perfusion image sequences accurately. In this paper, we treated the kinetics extracted model as a non-negative matrix factorization (NMF) problem. We extracted the respiratory kinetics of the ultrasound hepatic perfusion image sequences by non-negative matrix factorization (NMF). The technique involves using the NMF objective function to accurately extract respiratory kinetics. It was tested on simulative phantom and used to analyze 6 liver CEUS hepatic perfusion image sequences. The experimental results show the effectiveness of our proposed method in quantitative and qualitative.
Targeted nanobubbles have been reported to improve the contrast effect of ultrasound imaging due to the enhanced permeation and retention effects at tumor vascular leaks. In this work, the contrast enhancement abilities and the tumor targeting potential of a self-made VEGFR2-targeted nanobubble ultrasound contrast agent was evaluated <i>in</i>-<i>vitro</i> and <i>in-vivo</i>. Size distribution and zeta potential were assessed. Then the contrast-enhanced ultrasound imaging of the VEGFR2 targeted nanobubbles were evaluated with a custom-made experimental apparatus and in normal Wistar rats. Finally, the <i>in-vivo </i>tumor-targeting ability was evaluated on nude mice with subcutaneous tumor. The results showed that the target nanobubbles had uniform distribution with the average diameter of 208.1 nm, polydispersity index (PDI) of 0.411, and zeta potential of -13.21 mV. Significant contrast enhancement was observed in both <i>in-vitro </i>and <i>in-vivo </i>ultrasound imaging, demonstrating that the self-made target nanobubbles can enhance the contrast effect of ultrasound imaging efficiently. Targeted tumor imaging showed less promising result, due to the fact that the targeted nanobubbles arriving and permeating through tumor vessels were not many enough to produce significant enhancement. Future work will focus on exploring new imaging algorithm which is sensitive to targeted nanobubbles, so as to correctly detect the contrast agent, particularly at a low bubble concentration.
In this paper, the relationship between image intensity and ultrasound contrast agent (UCA) concentration is investigated. Experiments are conducted in water bath using a silicon tube filled with UCA (SonoVue) at different concentrations (100μl/l to 6000μl/l) at around 37 °C to simulate the temperature in human body. The mean gray-scale intensity within the region of interest (ROI) is calculated to obtain the plot of signal intensity to UCA concentration. The results show that the intensity firstly exhibits a linear increase to the peak at approximately 1500μl/l then appears a downward trend due to the multiple scattering (MS) effects.
Nanoscale bubbles (nanobubbles) have been reported to improve contrast in tumor-targeted ultrasound imaging due to the enhanced permeation and retention effects at tumor vascular leaks. In this work, a self-made nanobubble ultrasound contrast agent was preliminarily characterized and evaluated in-vitro and in-vivo. Fundamental properties such as morphology appearance, size distribution, zeta potential, bubble concentration (bubble numbers per milliliter contrast agent suspension) and the stability of nanobubbles were assessed by light microscope and particle sizing analysis. Then the concentration intensity curve and time intensity curves (TICs) were acquired by ultrasound imaging experiment in-vitro. Finally, the contrast-enhanced ultrasonography was performed on rat to investigate the procedure of liver perfusion. The results showed that the nanobubbles had good shape and uniform distribution with the average diameter of 507.9 nm, polydispersity index (PDI) of 0.527, and zeta potential of -19.17 mV. Significant contrast enhancement was observed in in-vitro ultrasound imaging, demonstrating that the self-made nanobubbles can enhance the contrast effect of ultrasound imaging efficiently in-vitro. Slightly contrast enhancement was observed in in-vivo ultrasound imaging, indicating that the nanobubbles are not stable enough in-vivo. Future work will be focused on improving the ultrasonic imaging performance, stability, and antibody binding of the nanoscale ultrasound contrast agent.
Images acquired in free breathing using contrast enhanced ultrasound exhibit a periodic motion that needs to be
compensated for if a further accurate quantification of the hepatic perfusion analysis is to be executed. In this work, we
present an algorithm to compensate the respiratory motion by effectively combining the PCA (Principal Component
Analysis) method and block matching method. The respiratory kinetics of the ultrasound hepatic perfusion image
sequences was firstly extracted using the PCA method. Then, the optimal phase of the obtained respiratory kinetics was
detected after normalizing the motion amplitude and determining the image subsequences of the original image
sequences. The image subsequences were registered by the block matching method using cross-correlation as the
similarity. Finally, the motion-compensated contrast images can be acquired by using the position mapping and the
algorithm was evaluated by comparing the TICs extracted from the original image sequences and compensated image
subsequences. Quantitative comparisons demonstrated that the average fitting error estimated of ROIs (region of interest)
was reduced from 10.9278 ± 6.2756 to 5.1644 ± 3.3431 after compensating.