Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects

One of the most widely used medical modality by healthcare industry is ultrasound imaging, which is often corrupted by multiplicative noise (known as speckle). The reduction of such kind of noise from ultrasound images is highly desirable for providing the proper diagnosis of a disease in real-time. The classical bilateral filter (CBF) is well known as most effective edge preserving and denoising filter for Gaussian noise reduction. Therefore, in this paper, a new speckle denoising filter is designed which is based on local statistics, Chi-square-based distance measure and box-based kernel function in bilateral filter framework for application and use in real time. The proposed speckle denoising scheme is tested on various synthetic, B-mode, simulated and real ultrasound images. The various quantitative and qualitative results suggest that the proposed local statistics-based bilateral filter (LSBF) outperforms the various existing speckle noise suppression techniques in term of denoising and restoration of fine textural information in the denoised images. The proposed LSBF method is compared with existing speckle noise reduction methods and experimental results demonstrate that, the proposed LSBF method have better noise removing and structure preserving capability as compared to existing standard denoising filters for speckle noise.

Local Statistics-based Speckle Reducing Bilateral Filter for Medical Ultrasound Images

A survey on regional level set image segmentation models based on the energy functional similarity measure

Saliency-guided automatic detection and segmentation of tumor in breast ultrasound images

Retrospective correction of intensity inhomogeneity with sparsity constraints in transform-domain: Application to brain MRI.

Guided trilateral filter and its application to ultrasound image despeckling

Mobile Edge Computing (MEC) migrates the computing center to the network edge to provide computing services for Mobile Users (MUs). However, due to the limited capacity of MUs and insufficient types of services stored by edge servers, it brings great challenges to computation offloading and service caching in Internet of Things (IoT) network. In this paper, we investigate the joint optimization problem of computation offloading, resource allocation and service caching replacement in a collaborative MEC system to minimize the total cost of all MUs under the latency and resource constraints. Accordingly, we formulate the problem as a Mixed Integer Non-linear Programming (MINLP) problem. In order to solve this problem, the Deep Deterministic Policy Gradient (DDPG)-based algorithm is proposed. The simulation results show that the proposed algorithm is better than other benchmark schemes in different scenarios.

DDPG-based Computation Offloading and Service Caching in Mobile Edge Computing

Intensity inhomogeneity is one of the major obstacles for intensity-based segmentation in many applications. The recently proposed kernel mapping (KM) method has exhibited excellent performance on segmenting various types of noisy images while it is not effective to handle intensity inhomogeneity. To overcome this drawback, this study presents a localised KM (LKM) method based on the fact that intensity inhomogeneity can be ignored in a local neighbourhood. The authors’ method first reconstructs the KM formulation of image segmentation in a neighbourhood of each pixel, and then such formulations for all pixels can be integrated together to derive the LKM energy functional. Minimisation of the energy functional is implemented by solving an equivalent convex-relaxed problem whose optimisation can be quickly achieved via the split Bregman method. Experimental results on two-phase segmentation and multiphase segmentation demonstrate competitive performance of the LKM method in the presence of intensity inhomogeneity and severe noise.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2017.0132

Convex-relaxed active contour model based on localised kernel mapping

A Weibull-distribution-based hybrid total variation method for speckle reduction in ultrasound images

To solve the problems of computational complexity and inaccuracy in classical vanishing point detection algorithms, such as the cascaded Hough transform, a vanishing point detection method based on constrained classification is proposed. First, the short line data are filtered to avoid interference in straight line detection, and then, the line segment is screened and classified by hierarchical clustering according to the image characteristics of the line segment and the variation pattern of angle similarity. Subsequently, Three types of straight line segments with the most significant angle differences are acquired. To prevent the optimization algorithm from getting stuck in the “wrong” local optimum neighborhood or failing to locate the global optimum, a set of constraints are set to further restrict the search. Afterward, the classified line segments are projected into a finite rhombic space, which are then quantified. The point with the maximum vote is eventually identified as the vanishing point. Experimental results show that the proposed method not only greatly reduces the computational complexity of vanishing points but also largely improves the accuracy of vanishing point detection.

/pdf/vanishing-point-detection-method-based-on-constrained-3nwi4ynr.pdf

Guoqiang Gong

Papers

Guided trilateral filter and its application to ultrasound image despeckling

DDPG-based Computation Offloading and Service Caching in Mobile Edge Computing

Convex-relaxed active contour model based on localised kernel mapping

A Weibull-distribution-based hybrid total variation method for speckle reduction in ultrasound images

Vanishing Point Detection Method Based on Constrained Classification for Checkpoints on Urban Roads