A lossy 3D wavelet transform for high-quality compression of medical video

In this paper, we present a lossy compression scheme based on the application of the 3D fast wavelet transform to code medical video. This type of video has special features, such as its representation in gray scale, its very few interframe variations, and the quality requirements of the reconstructed images. These characteristics as well as the social impact of the desired applications demand a design and implementation of coding schemes especially oriented to exploit them. We analyze different parameters of the codification process, such as the utilization of different wavelets functions, the number of steps the wavelet function is applied to, the way the thresholds are chosen, and the selected methods in the quantization and entropy encoder. In order to enhance our original encoder, we propose several improvements in the entropy encoder: 3D-conscious run-length, hexadecimal coding and the application of arithmetic coding instead of Huffman. Our coder achieves a good trade-off between compression ratio and quality of the reconstructed video. We have also compared our scheme with MPEG-2 and EZW, obtaining better compression ratios up to 119% and 46%, respectively for the same PSNR.     

A pointcut-based coverage analysis approach for aspect-oriented programs

Aspect-oriented programming (AOP) is a promising technology that supports separation of crosscutting concerns (i.e., functionality that tends to be tangled with, and scattered through the rest of the system). In AOP, a method-like construct named advice is applied to join points in the system through a special construct named pointcut. This mechanism supports the modularization of crosscutting behavior; however, since the added interactions are not explicit in the source code, it is hard to ensure their correctness. To tackle this problem, this paper presents a rigorous coverage analysis approach to ensure exercising the logic of each advice - statements, branches, and def-use pairs - at each affected join point. To make this analysis possible, a structural model based on Java bytecode - called PointCut-based Def-Use Graph (PCDU) - is proposed, along with three integration testing criteria. Theoretical, empirical, and exploratory studies involving 12 aspect-oriented programs and several fault examples present evidence of the feasibility and effectiveness of the proposed approach.     

TecDEM: A MATLAB based toolbox for tectonic geomorphology, Part 1: Drainage network preprocessing and stream profile analysis

We present TecDEM, a software shell implemented in MATLAB that applies tectonic geomorphologic tasks to digital elevation models (DEMs). The first part of this paper series describes drainage partitioning schemes and stream profile analysis. The graphical user interface of TecDEM provides several options: determining flow directions, stream vectorization, watershed delineation, Strahler order labeling, stream profile generation, knickpoints selection, Concavity, Steepness and Hack indices calculations. The knickpoints along selected streams as well as stream profile analysis, and Hack index per stream profile are computed using a semi-automatic method. TecDEM was used to extract and investigate the stream profiles in the Kaghan Valley (Northern Pakistan). Our interpretations of the TecDEM results correlate well with previous tectonic evolution models for this region. TecDEM is designed to assist geoscientists in applying complex tectonic geomorphology tasks to global DEM data.     

A review of vision-based systems for soccer video analysis

This paper presents a survey of soccer video analysis systems for different applications: video summarization, provision of augmented information, high-level analysis. Computer vision techniques have been adapted to be applicable in the challenging soccer context. Different semantic levels of interpretation are required according to the complexity of the corresponding applications. For each application area we analyze the computer vision methodologies, their strengths and weaknesses and we investigate whether these approaches can be applied to extensive and real time soccer video analysis.     

Integration testing of Object-Oriented and Aspect-Oriented programs: A structural pairwise approach for Java

Several testing approaches focus on finding faults in software units of implementation. A problem not addressed by unit testing is the interaction among units, with respect to the correctness of their interfaces. In this paper a structural integration testing approach for Object-Oriented (OO) and Aspect-Oriented (AO) Java programs is presented. To make the activity more feasible, we address the testing of pairs of units (i.e., methods and pieces of advice). A model called (PairWise Def-Use) graph to represent the flow of control and data between pairs of units is proposed. Based on the , the following family of testing criteria is defined: all-pairwise-integrated-nodes (control-flow based), all-pairwise-integrated-edges (control-flow based), and all-pairwise-integrated-uses (data-flow based). To evaluate the proposed approach, an implementation of the criteria in a testing tool is presented along with an example of usage and an exploratory study. The study with 7 AO programs that are also OO was conducted to investigate the cost of application and usefulness of the approach. Results provided evidence that the criteria are practical and useful for integration testing of OO and AO programs.     

The Hermite transform as an efficient model for local image analysis: An application to medical image fusion

The Hermite transform is introduced as an image representation model that can be used to tackle the problem of fusion in multimodal medical imagery. This model includes some important properties of human visual perception, such as local orientation analysis and the Guassian derivative model of early vision. Local analysis is achieved by windowing the image with a Gaussian function, then a local expansion into orthogonal polynomials takes place at every window position. Expansion coefficients are called Hermite coefficients and it is shown that they can be directly obtained by convolving the image with Gaussian derivative filters, in agreement with psychophysical insights of human visual perception. A compact representation can be obtained by locally steering the Hermite coefficients towards the direction of local maximum energy. Image fusion is achieved by combining the steered Hermite coefficients of both source images with the method of verification of consistency. Fusion results are compared with a competitive wavelet-based technique, proving that the Hermite transform provides better reconstruction of relevant image structures.     

Re-designing tasking structures of Ada programs for analysis: A case study

In previous publications the authors described a compositional (hierarchical) approach to reachability analysis of Ada tasking programs based on process algebra. The abstraction capabilities of process algebra provide an effective means to control state explosion in automated state-space analysis, but only if a design is carefully modularized to encapsulate details of behaviour. This paper reports experience modifying an existing design (a remote temperature sensor system described by Sanden) to make it more amenable to hierarchical analysis. Redesign for analysis was effective in improving the design in other ways as well: flaws uncovered in the analysis (and present in the original design) were easy to understand and correct because of the increased understandability of the revised design. This also suggests that these flaws might have been avoided, and the design generally improved, had ‘design for analysis’ been applied from the start.     

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review,two showcases and beyond

Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems’ black-box choices are made. This research field inspects the measures and models involved in decision-making and seeks solutions to explain them explicitly. Many of the machine learning algorithms cannot manifest how and why a decision has been cast. This is particularly true of the most popular deep neural network approaches currently in use. Consequently, our confidence in AI systems can be hindered by the lack of explainability in these black-box models. The XAI becomes more and more crucial for deep learning powered applications, especially for medical and healthcare studies, although in general these deep neural networks can return an arresting dividend in performance. The insufficient explainability and transparency in most existing AI systems can be one of the major reasons that successful implementation and integration of AI tools into routine clinical practice are uncommon. In this study, we first surveyed the current progress of XAI and in particular its advances in healthcare applications. We then introduced our solutions for XAI leveraging multi-modal and multi-centre data fusion, and subsequently validated in two showcases following real clinical scenarios. Comprehensive quantitative and qualitative analyses can prove the efficacy of our proposed XAI solutions, from which we can envisage successful applications in a broader range of clinical questions.