Due to the limitation of imaging technology, there is a lot of speckle noise in contrast-enhanced ultrasound (CEUS) images, which affects the improvement of resolution and the correct diagnosis made by doctors. It is difficult for existing methods to balance better image denoising and more detailed preservation. This paper presents a network combining bidirectional ConvLSTM (Convolutional Long Short-Term Memory) with 3D DnCNN to generate clearer images which we name BCDCNN. This makes full use of the correlation between CEUS video frame sequences. Firstly, we design an abnormal frame detection module to filter out abnormal frames from the video and combine several CEUS videos with synthetic CEUS videos to avoid over-fitting and improve the generalization ability of the model. Secondly, a three-dimensional convolution operator is imported to improve the two-dimensional DnCNN denoising model, so as to process image sequences on the time dimension. Finally, the bidirectional ConvLSTM is imported to learn the time characteristics of video, which makes full use of the inter-frame information. By observing the visual quality and various quantification indicators, our method performs better in denoising and detail preservation than the current mainstream methods, which is conducive to future medical analysis and diagnosis.

Video shot detection is an important contemporary problem since it is the first step toward automatic indexing, content based video retrieval and many other different applications. A novel shot boundary detection using wavelet and Support Vector Machine is proposed in this paper. Shot boundary detection algorithms work by extracting the color and the edge in different direction from wavelet transition coefficients. A multi-class support vector machine (SVM) classifier is used to classify the video shot into three categories: cut transition(CT), gradual transition(GT) and normal sequences (NF). To enhance the robustness of the algorithm, we form the feature vector from all frames within a temporal window. Numerical experiments using a variety of videos demonstrate that our method is capable of accurately detecting and discriminating shot transitions in videos with different characteristics.

Shot boundary detection is an important fundamental process in video analysis. A novel shot boundary detection is proposed in this paper. To improve the performance of the algorithm and reduce the computational cost, frames that are clearly not shot boundaries are first removed from the original video. After that, new features are proposed to capture the changing statistics of different kinds of shot transitions so as to identify, not only abrupt shot transitions, but also gradual transitions accordingly. At last, use different algorithms for different kinds of shot transitions to help us to get a better solution for shot boundary detection problem. Numerical experiments in a variety of videos demonstrate that our method is capable of accurately detecting shot transitions, and could greatly reduce the computational cost.

MOTIVATION: Flux estimation by using (13) C-labeling pattern information of metabolites is currently the only method that can give accurate, detailed quantification of all intracellular fluxes in the central metabolism of a microorganism. In essence, it corresponds to a constrained optimization problem which minimizes a weighted distance between measured and simulated results. Characteristics, such as existence of multiple local minima, non-linear and non-differentiable make this problem a special difficulty. RESULTS: In the present work, we propose an evolutionary-based global optimization algorithm taking advantage of the convex feature of the problem's solution space. Based on the characteristics of convex spaces, specialized initial population and evolutionary operators are designed to solve (13)C-based metabolic flux estimation problem robustly and efficiently. The algorithm was applied to estimate the central metabolic fluxes in Escherichia coli and compared with conventional optimization technique. Experimental results illustrated that our algorithm is capable of achieving fast convergence to good near-optima and maintaining the robust nature of evolutionary algorithms at the same time. AVAILABILITY: Available from the authors upon request.